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The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.

Penelitian ini bertujuan untuk mengembangkan sistem telemetri guna memantau kinerja panel surya pada beberapa lokasi dengan menggunakan komunikasi jarak jauh dan platform Internet of Things (IoT). Metode pemantauan konvensional memiliki keterbatasan dalam menyediakan data secara real-time pada area yang luas, sehingga evaluasi kinerja jarak jauh menjadi kurang efisien. Untuk mengatasi permasalahan tersebut, dirancang sebuah sistem pemantauan menggunakan mikrokontroler ESP32, sensor INA219 untuk mengukur tegangan dan arus, modul GPS Neo-M8 untuk identifikasi lokasi, modul Real-Time Clock (RTC) DS3231 untuk pencatatan waktu, serta modul LoRa RA-02 sebagai media komunikasi nirkabel. Setiap node pengirim dilengkapi dengan modul MicroSD untuk menyimpan data pengukuran secara lokal. Data hasil pengukuran dikirimkan melalui LoRa ke unit penerima dan ditampilkan secara real-time pada platform Thinger.io. Hasil kalibrasi menunjukkan bahwa sensor INA219 memiliki rata-rata galat pengukuran arus sebesar 0,71% dan galat pengukuran tegangan sebesar 0,1%. Pengujian GPS menunjukkan koordinat lokasi yang stabil dengan tingkat akurasi sekitar ±3 hingga ±8 meter. Seluruh data pengukuran berhasil dikirim, disimpan, dan ditampilkan tanpa kehilangan data yang signifikan. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu menyediakan pemantauan parameter panel surya secara jarak jauh yang andal dan efisien dalam kondisi lapangan.

This paper explores the epistemological dimensions of the digital transformation occurring in traditional game development through the integration of machine learning systems. By examining how knowledge creation, validation, and application have evolved in this domain, we identify fundamental shifts in the epistemological frameworks governing game development practices. The research investigates how machine learning has redefined creative processes, technical implementation, and experiential design while challenging traditional notions of authorship, expertise, and knowledge transmission. Through analysis of industry case studies, technological capabilities, and theoretical frameworks, this paper contributes to understanding how machine learning systems are not merely tools but epistemological agents that fundamentally transform how knowledge is generated, validated, and utilized in game development ecosystems.

Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.

The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

Polusi udara partikulat halus (PM2,5) merupakan ancaman serius bagi kesehatan masyarakat di Kabupaten Brebes, Jawa Tengah. Faktor penyumbang utamanya adalah emisi kendaraan di jalur Pantura, aktivitas industri perikanan, serta konsentrasi tinggi selama musim kemarau (Juni–November). Tidak adanya model peramalan sub-jam yang akurat menghambat pengembangan sistem peringatan dini yang efektif. Penelitian ini mengembangkan dan mengevaluasi model deep learning berbasis Transformer untuk memprediksi konsentrasi PM2,5 dengan resolusi waktu 15 menit. Data yang digunakan berasal dari NASA GEOS-CF (band PM25_RH35_GCC) yang diakses melalui Google Earth Engine menggunakan API Python. Dataset mencakup periode 1 Januari hingga 22 November 2025, menghasilkan 7.813 observasi per jam, yang kemudian diinterpolasi linear menjadi 31.249 titik data dengan resolusi 15 menit. Arsitektur Transformer terdiri dari 3 lapis enkoder, 4 kepala perhatian multi-head, dimensi embedding 128, dimensi feed-forward 256, panjang sekuen 60 timestep, dan augmentasi fitur menggunakan rerata bergulir (*rolling mean*, jendela = 3) dan beda pertama (*first difference*). Pelatihan dilakukan dengan TensorFlow-Keras, pengoptimal Adam, penjadwal peluruhan kosinus (*cosine decay scheduler*), dan fungsi kerugian Huber. Pembagian data dilakukan secara kronologis: 70% pelatihan, 30% validasi. Evaluasi pada set uji independen (16 Agustus–21 November 2025, 9.357 observasi atau 97 hari 11 jam 15 menit) menghasilkan MAE 0,7691 µg/m³, RMSE 1,2052 µg/m³, R² 0,9945, dan *Explained Variance Score* 0,9948. Model ini mampu menggambarkan variasi diurnal dan anomali musiman secara akurat, jauh melampaui model LSTM dan GTWR konvensional. Penelitian ini memberikan kontribusi signifikan di bidang Teknologi Informasi melalui kerangka kerja pengolahan *big data* satelit untuk aplikasi lingkungan.

Hijaiyah letters have varying shapes, and some of them are very similar, often causing errors in the manual character recognition process. This study aims to classify Hijaiyah letters based on digital images using the Convolutional Neural Network (CNN) method. This method was used in this study with a dataset consisting of 28 letter classes and a total of 4,480 images obtained from various public sources and private data. All images underwent a preprocessing stage that included labeling, resizing, normalization, and augmentation, then were divided into three parts, namely training data, validation data, and test data with a ratio of 70:20:10. The training process was carried out using the Python programming language with the help of the TensorFlow and Keras libraries on the Google Colab platform. The test results showed that the CNN model achieved an accuracy of 97.10%, with an average precision, recall, and F1-score of 0.97, respectively. Classification errors only occurred in letters that had similar shapes, such as Syin and Sin. Based on these results, the CNN method proved to be effective, efficient, and accurate in recognizing Hijaiyah letter image patterns, so it can be used as a basis for developing classification models with higher accuracy in the future.

The increasing global energy demand drives the search for efficient and sustainable renewable energy solutions. Solar panels have become one of the most widely used technologies; however, their efficiency remains limited when installed in a static position. This research aims to analyze the performance of a single-axis auto tracking system on a 10WP solar panel integrated with the Internet of Things (IoT) for real-time monitoring, specifically in powering a portable powerbank. The research method employed was a quantitative experimental design with three testing scenarios: powerbank charging using an auto-tracking solar panel, a static solar panel, and conventional household electricity as a comparison. Charging data were collected via an IoT system integrated with the Blynk application in real-time. The results indicate that the auto-tracking system increased charging efficiency by around 10%, compared to only 6% with a static panel in one hour. This performance is nearly equal to household electricity charging, which reached approximately 10–11%. The study concludes that the single-axis IoT-based auto-tracking system significantly enhances the performance of small-scale solar panels and holds strong potential for portable energy solutions in remote areas.

Suhu dan kelembaban merupakan parameter lingkungan yang harus dijaga pada ruang kubikel untuk memastikan peralatan distribusi listrik tetap bekerja secara optimal. Pada multi-kubikel, perbedaan fungsi dan beban menyebabkan karakteristik suhu dan kelembaban pada tiap ruang kubikel tidak sama, sehingga pemantauan secara manual menjadi kurang efektif dan efisien. Penelitian ini bertujuan untuk merancang dan membangun prototype sistem monitoring dan kontrol suhu-kelembaban pada multi-kubikel berbasis Internet of Things (IoT) yang terdiri dari tiga buah kubikel. Sistem ini menggunakan ESP8266 sebagai mikrokontroler utama dan sensor DHT20 sebagai sensor suhu dan kelembaban yang masing-masing dipasang pada kubikel dengan kondisi lingkungan berbeda. Sistem dilengkapi dengan aktuator kipas dan lampu, serta notifikasi real-time melalui LCD dan Telegram. Meskipun kontrol dan monitoring dilakukan secara terpisah pada tiap kubikel, notifikasi kondisi seluruh kubikel terintegrasi pada satu kanal Telegram yang sama. Pengujian kinerja sistem dengan memberikan variasi suhu dan kelembaban yang berbeda untuk tiap kubikel. Kubikel 1 diberi kondisi normal (suhu 35°C-40°C dan kelembaban 50%-70%), kubikel 2 diberi kondisi overheat (suhu di atas 40°C), sedangkan kubikel 3 diberi kondisi overhumidity (kelembaban > 70%). Hasil pengujian menunjukkan sistem mampu melakukan kontrol suhu dan kelembaban dalam ruang multi-kubikel serta mengirimkan notifikasi melalui Telegram dengan tingkat keberhasilan 100% dan rata-rata delay 5,6 detik.

Waste has become a serious environmental problem in Indonesia, which continues to increase along with population growth. The issue of waste management poses serious challenges for the environment, especially in the process of separating organic and inorganic waste. In the field of computer vision, recognising the type and shape of waste through camera images remains a challenge due to variations in shape, colour, and complex lighting conditions. Therefore, this problem utilises Deep Learning technology, which is expected to be widely applied in Indonesia, especially in large cities with high waste volumes. This study aims to distinguish between organic and inorganic waste using the Convolutional Neural Network (CNN) method based on digital images. The developed CNN model was trained to recognise the visual patterns of each type of waste and tested to measure its accuracy. The test results show that the CNN-based classification system is capable of achieving an accuracy rate of 95%, thus proving the effectiveness of this method in supporting artificial intelligence-based automatic waste sorting systems.

Relational databases store information in interconnected tables and are widely used for data management and retrieval. However, in certain environments, the original values stored in a relational database cannot be exposed during data retrieval. This limitation creates a challenge because common encryption methods only transform data for storage and do not support mathematical operations needed for value matching. Partially Homomorphic Encryption is a cryptographic approach that allows specific mathematical operations to be performed directly on transformed data without restoring it to its original form. This study proposes the use of Partially Homomorphic Encryption to enable value-based data retrieval while keeping all stored values in their transformed form throughout the entire process. The method relies on homomorphic properties that allow mathematical comparison to be conducted on encrypted data, making the retrieval process possible without revealing the original values. The results show that this approach can perform data retrieval operations in a relational database while preserving the transformed structure of the stored data. The proposed method offers an alternative for environments that require data retrieval without exposing original values and demonstrates the potential of homomorphic techniques in supporting secure and functional data processing in relational database contexts.

Given the great benefits of electrical energy, the availability of electrical energy sources is limited. Currently, the availability of electrical energy sources is not able to meet the increasing demand for electricity in Indonesia. The high use of electrical energy in daily life will have a negative impact on the environment. Therefore, to maintain the sustainability of energy sources, it is necessary to pursue strategic steps that can support the provision of electrical energy optimally and affordably, This study aims to find out how much total energy is used by the Energy Consumption Index (IKE) on electrical energy from the influence of electrical power and the length of time of use of electrical energy at SMK Negeri 2 Lubuk Basung, Lubuk Basung District, Agam Regency. This study aims to determine the value of energy consumption used or Energy Consumption Index (IKE) and energy saving opportunities at SMK Negeri 2 Lubuk Basung, Lubuk Basung District, Agam Regency. The results of this study are for the IKE value of the first floor which is 1.71 kWh/m2, for the IKE value of the second floor which is 0.03 kWh/m2, for the IKE value of one building, which is with a value of 1.74 kWh/m2, for the annual IKE of 0.022 kWh/m2/year and for the value of energy-saving opportunities of IDR 651 646/month IDR 7 819 755/year.

Perkembangan sistem informasi seiring dengan kemajuan teknologi informasi yang signifikan, berperan dalam penyimpanan, pengelolaan, dan pendistribusian informasi. Salah satu contohnya adalah dalam pengelolaan data instalasi kabel serat optik. Dinas Komunikasi dan Informatika Bangkalan, sebagai instansi pemerintah, menghadapi tantangan dalam pencatatan data serat optik yang masih dilakukan secara konvensional menggunakan perangkat lunak Excel. Untuk mengatasi masalah ini, peneliti merancang dan mengembangkan "Sistem Informasi Instalasi Kabel Serat Optik di Kabupaten Bangkalan." Tujuan dari sistem ini adalah untuk meningkatkan efisiensi dan efektivitas dalam pengelolaan data, menyederhanakan proses pencatatan, dan mempercepat akses informasi kepada pihak terkait. Hasil pengelolaan data dalam sistem informasi ini mencakup empat proses utama: pengelolaan data titik instalasi, pengelolaan data lokasi, pengelolaan data pengguna, dan pengelolaan data kecepatan internet. Penerapan metode waterfall dalam perancangan sistem informasi ini memberikan solusi untuk meningkatkan efisiensi dan efektivitas dalam pengelolaan data. Perancangan sistem informasi ini terdiri dari lima (5) perancangan utama yaitu: 1) Menampilkan halaman pengunjung ketika aplikasi diakses, 2) Menerima dan mengolah data berdasarkan tindakan CRUD (create, read, update, delete), 3) Melakukan verifikasi data login dan menampilkan pesan error apabila data login salah, 4) Menyediakan fitur pencarian data, dan 5) Menghasilkan laporan data instalasi.

Penelitian ini bertujuan merancang dan mengembangkan Sistem Informasi Penjualan berbasis web untuk Gerai Oleh-Oleh Bali yang selama ini masih menggunakan pencatatan manual dengan buku besar dan perhitungan menggunakan kalkulator. Sistem manual tersebut menyebabkan risiko kesalahan pencatatan, lambatnya proses pencarian data, serta kesulitan dalam pengarsipan data penjualan, pembelian, pemesanan, dan pengelolaan stok barang. Penelitian menggunakan metode pengembangan perangkat lunak Waterfall dengan pendekatan pemodelan Unified Modeling Language (UML). Tahapan yang dilakukan meliputi analisis kebutuhan melalui wawancara dengan pemilik gerai, perancangan sistem (Use Case Diagram dan Class Diagram), implementasi, pengujian unit, pengujian sistem menggunakan metode black-box testing, serta tahap pemeliharaan (maintenance). Sistem yang dibangun mencakup fitur login, dashboard, pengelolaan data master (supplier dan barang), transaksi penjualan dengan dukungan scan barcode, transaksi pembelian, laporan penjualan dan pembelian, serta pengelolaan user. Hasil pengujian black-box menunjukkan seluruh test case berstatus Valid dan sistem berfungsi sesuai harapan. Pada tahap maintenance dilakukan contoh corrective maintenance dengan perbaikan bug pada query laporan penjualan harian.Sistem informasi penjualan berbasis web yang dihasilkan mampu mempercepat proses transaksi, mengurangi kesalahan manusia, meningkatkan akurasi data, serta memudahkan pengelolaan stok dan pembuatan laporan secara real-time. Implementasi sistem ini memberikan solusi efektif bagi Gerai Oleh-Oleh Bali dalam meningkatkan efisiensi operasional dan interaksi dengan pelanggan.

The rapid expansion of e-commerce in Indonesia has resulted in a significant rise in the number of customer reviews, which serve as a valuable source of insight for understanding consumer satisfaction. This study aims to classify or identify sentiments from product reviews on the Tokopedia platform into three categories, using the Support Vector Machine algorithm. The classification method data were ethically collected through web scraping and include review text, ratings, and the number of “likes.”  The preprocessing stage involved several NLP techniques such as pre-procesesing data representation was generated using the Term Frequency–Inverse Document Frequency method, while the issue of class imbalance was addressed using the Synthetic Minority Over-sampling Technique.  Based on the test results, the SVM model achieved an accuracy of 79.48% on the test data using a linear kernel, showing the best performance in classifying positive sentiments. However, the classification of neutral and negative sentiments still requires improvement. This study demonstrates that the combination of the TF-IDF method, additional numerical features, and data balancing techniques can produce an an efficient sentiment analysis model within the e-commerce domain.

Indonesian Sign Language (BISINDO) serves as a primary communication medium for the deaf community; however, limited public understanding often creates barriers during daily interactions. This study aims to develop a real-time BISINDO word-level translation system using hand landmark extraction and temporal modeling with Long Short-Term Memory (LSTM). The system employs MediaPipe Hands to detect 21 hand landmarks per frame, which are then processed as sequential motion patterns to classify five BISINDO words: saya, terima kasih, maaf, nama, and kamu. A total of 250 gesture samples were recorded under controlled lighting conditions as the primary dataset. The processed sequences were used to train the LSTM model, which was subsequently integrated with an ESP32 microcontroller and a DFPlayer Mini module to produce direct audio output. Experimental results show that the model achieved an average accuracy of 86%, with precision and recall values ranging from 0.81 to 0.94. The confusion matrix analysis indicates that most gestures were correctly classified, although some errors occurred in gestures with similar initial motion trajectories. Integration testing demonstrated an average system latency of 3.8 seconds and an audio output success rate of 85%. These findings indicate that the proposed system is capable of translating BISINDO word-level gestures accurately, responsively, and consistently in real-time conditions. This study provides a strong foundation for the broader development of sign language translation systems, with potential enhancements in vocabulary expansion, multi-user datasets, and hardware optimization for deployment in real-world environments.

The problems in rice fields are complex and varied, depending on geographic location, rice variety, and growing season. Pests often cause serious economic losses. The Solar Sonic Repeller is an innovative portable pest control device designed to address pest problems by utilizing renewable energy, specifically solar energy. This product aims to offer an environmentally friendly and efficient solution. It works by emitting ultrasonic sound waves with a frequency of 30,000–40,000 Hz. The device's advantages lie in its portability and energy independence, thanks to the use of a charging module powered by an integrated photovoltaic (PV) panel with automatic battery charging during the day. The first test measured the output frequency using an oscilloscope to verify that the oscillator circuit produced waves at the specified frequency. The second test measured the device's effectiveness by examining the pest response to the device at various distances. This test was effective within a maximum radius of approximately 14 m from the center point, covering a rice field area of ​​250 m2.

PAMTIRTA Tempino is an institution that provides clean water services in the Tempino area. The process of recording water use and monitoring water turbidity is still done manually, making it prone to recording errors and making it difficult to monitor the water quality distributed to the community. This study aims to design a website-based water turbidity recording and monitoring system by focusing on User Interface (UI) and User Experience (UX) aspects using the Design Thinking method. The research follows five stages of Design Thinking: empathize, define, ideate, prototype, and test. Data collection involves observation and in-depth interviews with PAMTIRTA officers. The results include a design with key features such as digital water meter recording, turbidity monitoring dashboards, and complaint services. The prototype was tested using Maze and the System Usability Scale (SUS), achieving a score of 80.1 and falling into the "Good" category (grade B). These results demonstrate that the UI/UX design effectively provides an easy-to-understand, operationally suitable, and efficient solution for PAMTIRTA Tempino's water recording and turbidity monitoring needs. This design offers a ready-to-implement solution to improve the efficiency, accuracy, and quality of clean water services in the Tempino area.

Oxygen is vital therapy where delivery accuracy is crucial, especially for infant patients, to ensure treatment effectiveness and prevent the risks of hypoxia or toxicity. With the implementation of the mandatory Domestic Product Utilization Policy (TKDN+BMP ≥ 40%), evaluating the quality of local products has become an urgent necessity. This study aims to test and analyze the quality and accuracy of domestically produced infant oxygen flowmeters compared to an imported product. The method used was experimental testing, measuring three brands of domestic products and one brand of foreign product at flow rate settings of 0.5, 1, 1.5, and 2 liters per minute (LPM). Each setting point was measured 10 times using a standardized calibrator to ensure data reliability. The measurement results were analyzed to identify the deviation level of each product. The findings of this study are expected to provide an objective conclusion on the quality equivalence of domestic products with imported ones and to identify which product has the lowest deviation rate. This can serve as scientific consideration for hospitals in selecting high-quality infant oxygen flowmeters, thereby supporting the domestic product policy.