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Behind the success of the Free Nutritious Meal Program (MBG), there are several problems related to the health factors of the program targets, namely, there are several cases of allergies that occur in schools, inadequate understanding of allergen management owned by food processing vendors, and the high cost of laboratory tests and the process that takes a long time. So, to overcome these problems, an application is proposed that can help detect allergens in food products using data mining and machine learning approaches. SVM and AdaBoost algorithms each have advantages that can be used to help build an optimal allergen detection model. This research uses a cross-validation model validation method with a value of K = 10 to help improve the performance of the model built. In this study, from the entire fold, an average accuracy value of 98.74% was obtained. To evaluate the model built, this research has also conducted several new data inputs, and in each new data input, the accuracy value is obtained above 99%. This indicates that the model built, namely the combination of SVM and AdaBoost algorithms with the cross-validation model validation method, produces high accuracy, so this model can greatly assist the allergen detection process in food products.

This study aims to analyze the factors that influence students' interest in continuing their education to college using a machine learning approach. Data was collected through an online questionnaire completed by 727 students between July 27 and August 22, 2025, covering 23 variables consisting of respondent identity (gender, grade level, major) as well as internal and external factors such as parental support, learning motivation, and preferred type of college. The data preparation stage was carried out through column cleaning, deletion of empty data, encoding of categorical variables, and division of the dataset into 80% training data and 20% test data. The Naive Bayes algorithm of the CategoricalNB type was used because it was suitable for the categorical nature of the data. The evaluation results showed that the model was able to predict student interest with 96% accuracy. For the class of students interested in continuing their studies, the precision, recall, and F1-score values were above 0.95, while the performance in the class of students who were not interested was slightly lower due to the smaller amount of data. These findings show that Naive Bayes is proven to be effective and reliable in classifying students' interest in continuing their studies and can be the basis for decision-making in designing more targeted educational strategies.

Since 2022, the application of the Internet of Things (IoT) in the healthcare sector has grown significantly, marked by the increasing adoption of wearable technology, artificial intelligence (AI), machine learning (ML), and blockchain integration. Research highlights India and China as leading contributors in this domain. IoT enables real-time monitoring of chronic diseases, tracking of patient vital signs, and detection of health protocol compliance. Integrated systems such as Monit4Healthy and RADAR-IoT support personalized medical recommendations and cross-platform interoperability. However, key challenges persist, including patient data privacy and security, system interoperability issues, data fragmentation, and barriers to user acceptance due to cost, digital literacy, and device comfort. Proposed solutions include blockchain for secure data sharing, adaptive congestion control for network performance, and user training to improve technology adoption. Therefore, successful IoT deployment in healthcare requires a comprehensive approach that addresses technological, social, ethical, and sustainability aspects to achieve an effective and inclusive transformation of health services.

This study compares the effectiveness of MAUT (Multi-Attribute Utility Theory), SMART (Simple Multi-Attribute Rating Technique), and WASPAS (Weighted Aggregated Sum Product Assessment) methods in a decision support system for determining the best employees at Sisilia Boutique. The quality of human resources is crucial in the retail business, but performance evaluation is often influenced by subjectivity. To address this, a multi-criteria-based decision support system is needed. MAUT translates preferences into a numerical scale, SMART calculates the average value of attributes based on weights, while WASPAS combines weighted summation (WSM) and weighted multiplication (WPM) for more balanced results. Employee performance data from Sisilia Boutique in June 2025, including attendance, store layout, customer service, and discipline, were used as the research object. The comparison results show consistency in the highest (K3) and lowest (K7) ratings across the three methods, with differences in the middle ratings. WASPAS offers a more balanced distribution of final scores, making it a comprehensive alternative for performance evaluation.

The rapid development of information technology in today's digital era has significantly impacted organizational performance, particularly in data management and resource planning. One organization that heavily relies on accurate data availability is the Indonesian Red Cross (PMI), especially its Blood Donor Unit (UDD). UDD PMI of Asahan Regency faces challenges in determining monthly blood donor targets to maintain stable blood stock. A shortage of blood supply can be fatal for patients requiring transfusions. Therefore, a system is needed to forecast the number of blood donors, allowing for more accurate decision-making. This study utilizes the Weighted Moving Average (WMA) method to predict the number of blood donors for the following month based on historical data from March 2024 to March 2025. The WMA method is chosen for its ability to assign greater weight to recent data, making the forecast more relevant and accurate. The results of this research are expected to assist UDD PMI Asahan Regency in anticipating blood needs and maintaining optimal stock availability.

Credit card fraud is a critical issue in digital financial transactions. This study aims to develop and evaluate fraud detection models using Logistic Regression and Gradient Boosting on an imbalanced dataset, where fraudulent transactions constitute only a small portion of the data. To address this imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. Logistic Regression, used as a baseline model, achieved 95% accuracy, 78.6% precision, 55.9% recall, and a 65.3% F1-score. After applying class weighting and SMOTE, recall improved to 88.7%, but precision dropped to 52%, indicating that the model became overly sensitive and prone to false positives. Gradient Boosting initially produced better results, with 98% accuracy, 95.5% precision, 84.3% recall, and an 89.5% F1-score. After hyperparameter tuning and resampling, its performance improved further to 96.7% precision, 86.1% recall, and a 91.1% F1-score. These results indicate that Gradient Boosting is more effective in handling imbalanced data and offers greater reliability in detecting fraudulent transactions. The findings support the growing evidence in favor of ensemble learning techniques in fraud detection applications. This research contributes practical insights into improving the accuracy and security of machine learning-based fraud detection systems in financial services.

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

As a vocational education institution, SMK Swadhipa 1 Natar is required to provide adequate facilities to support the development of its students' technical and practical skills. Although some facilities are already available, student complaints remain regarding the condition, availability, and utilization of these services, particularly those related to information technology.This study aims to analyze the level of student satisfaction with information technology services at SMK Swadhipa 1 Natar using a combination of Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA) methods. The study was conducted through a quantitative approach by distributing questionnaires to 100 respondents selected using stratified random sampling techniques. The data collected were analyzed to determine the overall satisfaction score and identify factors of information technology services that were a priority for improvement. The results of the CSI analysis showed that the level of student satisfaction with school information technology services was in the good category, with an average score of 82%. Furthermore, the results of the IPA analysis revealed that information technology services such as computer services in the school lab, wifi networks, and school websites consisting of school exam applications, student registration applications and information about the school on the website were in the top priority quadrant because they had a high level of importance but their performance was still low. Based on these results, it can be concluded that although in general students stated that they were quite satisfied with the information technology services available, there were several important aspects, especially technology-based information technology services, that needed more attention from the school. Thus, recommendations for improving technological infrastructure and periodic evaluation of educational information technology services can help SMK Swadhipa 1 Natar in improving the quality of educational services and student satisfaction. 

This study aims to evaluate and compare the performance of two neural network architectures under the Recurrent Neural Network (RNN) category, namely Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), in predicting earthquake magnitude in Indonesia. The dataset used consists of daily earthquake magnitude records from 2008 to 2023, preprocessed into time series format and normalized using the MinMax method. The training process was conducted using various combinations of batch size and epoch, and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and relative prediction accuracy. The evaluation results show that LSTM with a batch size of 32 and 50 epochs provides the best prediction performance, achieving a MAE of 0.2227 and 93.65% accuracy. Meanwhile, GRU performed optimally at a batch size of 64 and 50 epochs, with a MAE of 0.2229 and 93.66% accuracy. The prediction visualization shows that LSTM offers greater stability and precision in tracking actual data patterns. These findings indicate that LSTM holds stronger potential for supporting earthquake prediction systems based on time series data.

This study compares two service management models to evaluate the governance of the Wi-Fi network in Dusun Gita Nagari Baru. The main objective is to measure user satisfaction and service quality following the implementation of the COBIT 2019 framework, particularly the DSS02 domain (Manage Service Requests and Incidents). The research employed a mixed methods approach, using historical-comparative document analysis and Likert scale questionnaires distributed to 21 active users. The data were analysed through gap analysis, capability level mapping, and descriptive statistical analysis to identify performance differences between two periods. The results indicate that most indicators in the COBIT 2019 capability model are at Level 4 (Predictable), one indicator reaches Level 5 (Optimising), and another indicator is at Level 3. Indicators directly related to the DSS02 domain, such as ease of reporting, response speed, schedule accuracy, and repair time, demonstrate the most significant improvements. These findings support the hypothesis that implementing COBIT 2019-based governance for DSS02 can enhance user satisfaction and the quality of Wi-Fi network services in rural areas. This study also provides practical recommendations for the sustainable management of digital infrastructure in areas with limited access.

This study aims to analyze and compare the performance of the Naive Bayes, K-Nearest Neighbors (KNN), and Decision Tree algorithms in predicting the purchase intention of e-commerce visitors using the Online Shoppers Purchasing Intention Dataset, which consists of 12,330 records and 18 variables, with the Revenue variable serving as the classification target. The preprocessing stage involved transforming categorical and boolean variables into numerical form, standardizing features using StandardScaler, and splitting the dataset into 80 percent training data and 20 percent testing data. Model evaluation was conducted using accuracy, precision, recall, F1-score, and ROC-AUC metrics, and was further strengthened by 10-fold cross-validation to obtain more stable results. The findings indicate that KNN achieved the highest accuracy of 0.866180, while Naive Bayes produced the highest recall value of 0.690998 and the highest ROC-AUC value of 0.821696. Meanwhile, Decision Tree demonstrated relatively balanced performance with an accuracy of 0.857259 and an F1-score of 0.571776, whereas the cross-validation results identified KNN as the model with the highest average accuracy of 0.8770. These findings suggest that the selection of a classification model for purchase intention prediction cannot rely solely on a single evaluation metric, as each algorithm possesses different strengths. Therefore, a comparative approach among algorithms can help determine the most suitable model for supporting consumer behavior analysis on e-commerce platforms.

This study evaluates the performance of three community detection algorithms—Leiden, Infomap, and Label Propagation—on the legal network of the Republic of Indonesia spanning the period 2014–2024. The network consists of 679 nodes and 2,295 edges, constructed based on citation relationships among regulations. The evaluation employs four network topology metrics: modularity, coverage, conductance, and inter-cluster density. Results show that the Leiden algorithm achieves the highest modularity score (0.522991), indicating the formation of communities with strong internal density. Additionally, it yields the lowest conductance value (0.302455), suggesting relatively well-isolated communities. In contrast, the Label Propagation algorithm produces the highest coverage (0.835294) and inter-cluster density (0.542331), but with a lower modularity (0.431583), reflecting the formation of large communities with less distinct boundaries. Infomap exhibits moderate performance, with a modularity score of 0.508406 and inter-cluster density of 0.420803, yet records a relatively high conductance (0.410409). Network visualizations reveal three major communities for each algorithm, representing thematic clusters such as institutional governance, constitutional law, and public finance. Overall, the Leiden algorithm is considered the most optimal for detecting modular, stable, and thematically coherent community structures within the complex and interrelated network of Indonesian laws.

Penilaian kinerja karyawan menggunakan metode Balanced Scorecard (BSC) sangat efektif dalam mengukur penilaian kinerja karyawan dari berbagai perspektif, seperti finansial, pelanggan, proses internal dan pertumbuhan dan pembelajaran. Dengan demikian, penelitian ini bertujuan untuk merancang dan mengimplementasikan aplikasi penilaian kinerja karyawan dengan menggunakan metode Balanced Scorecard (BSC) pada perusahaan PT. ForIT Asta Solusindo. Pada penelitian ini penulis merancang dan mengimplementasikan aplikasi penilaian kinerja karyawan menggunakan metode Balanced Scorecard (BSC) dengan empat perspektif utama, yaitu finansial yang diterapkan pada divisi akuntan, pelanggan yang diterapkan pada divisi pemasaran; proses internal dan pertumbuhan dan pembelajaran pada berbagai divisi. Untuk masing-masing perspektif memiliki nilai bobot yang berbeda namun dengan jumlah keseluruhan bobot yang sama dengan nilai persentase yang mencapai 100%. Aplikasi ini berbasis web dengan menggunakan metode pengembangan perangkat lunak Agile dengan model Scrum untuk memastikan adaptabilitas dan kualitas produk. Aplikasi ini juga dibangun menggunakan framework Laravel dan menggunakan basis data MySQL sebagai tempat penyimpanan data. Hasil dari keseluruhan pengujian unit yang dilakukan dengan pengujian whitebox pada kode internal menggunakan PHPUnit. Hasil dari pengujian blackbox sesuai dengan kebutuhan fungsional dan non-fungsional aplikasi. Hasil dari skor Likert pada pengujian User acceptance test (UAT) menunjukkan dari faktor kepuasan pengguna mencapai 78% dan faktor kemudahan pengguna mencapai 96%. Kata Kunci: Balanced Scorecard (BSC), Key Performance Indicators (KPI), Penilaian kinerja, Perspektif, Agile Scrum.  

Pengembangan sistem informasi akademik bertujuan untuk memberikan sarana dasar kepada perguruan tinggi. Sistem informasi akademik diperlukan untuk mengelola seluruh kegiatan akademik diantaranya mengelola kartu rencana studi, mengelola kartu hasil studi, mengelola data dosen dan tenaga kependidikan, mengelola data mahasiswa, mengelola kelas, mengelola pertemuan dan presensi. Memiliki sistem informasi akademik akan mengurangi resiko keamanan data serta mendukung kemandirian pengelolaan teknologi informasi. Pengembangan sistem informasi ini menggunakan metode penghimpunan data menggunakan metode tanya jawab (interview) dan pengamatan lapangan. Performance, Information, Economics, Control, Efficiency dan Service adalah metode PIECES yang akan digunakan dalam menganalisa sistem. Unified Modelling Language (UML) adalah metode perancangan sistem yang digunakan dalam penelitian ini. System engineering, Requirement analysis, Design, Coding, Testing dan Maintenance adalah metode pengembangan sistem Waterfall yang peneliti gunakan. Sistem informasi akademik dikembangkan sesuai kebutuhan institusi saat ini tetapi seiring perkembangan peraturan dan kebutuhan institusi maka perlu dilakukan evaluasi kinerja secara periodik.

Aset sangat penting bagi suatu organisasi karena dapat menunjang kegiatan yang dilakukan seperti pada Yayasan Assanusiyah. Banyaknya lembaga pendidikan dan perusahaan yang dikelola, aset yang dimiliki Yayasan pun cukup banyak. Supaya aset dapat bernilai tinggi, perlu adanya pengelolaan aset. Kegiatan inventarisasi merupakan salah satu pengelolaan aset yang telah dilaksanakan oleh Yayasan Assanusiyah. Namun kegiatan inventarisasi masih dilakukan secara manual dan tidak terintegrasi. Sehingga data aset Yayasan tidak realtime dan sering terjadi kehilangan maupun penumpukan aset pada satu lokasi. Tujuan dari penelitian ini yaitu untuk menganalisis kebutuhan sistem informasi yang tepat untuk membantu kegiatan inventarisasi aset Yayasan dengan menggunakan teori The Unified of Acceptance and Use of Technology (UTAUT) yang terdiri dari empat dimensi, diantaranya performance expectancy, effort expectancy, social influence, dan facilitating conditions. Metode yang digunakan dalam penelitian ini yaitu metode penelitian deskriptif dengan menggunakan pendekatan kualitatif dan kuantitatif. Teknik pengumpulan data yang digunakan yaitu wawancara, observasi, dan studi dokumentasi. Penelitian ini menghasilkan data mengenai kebutuhan sistem informasi yang sesuai untuk digunakan dalam pelaksanaan inventarisasi aset di Yayasan Assanusiyah.

BLDC motor applications used in various forms in instrumentation, robotics, household, and transportation. One application of transportation equipment used as a propeller of electric bicycle vehicles. The value of the bicycle vehicle adjusted to the speed set, the amount that has determined. The purpose used in this study is to improve the efficiency of the regulation of BLDC motors on electric bicycles. Indicators of increasing performance are increasingly steady-state errors, and transient response required. The method used in this research is to do mathematical modeling in the form of transfer and optimization function equations. The model used is the model with the structure of the transfer function, while the optimization method used in this study is the Ziegler-Nichols method and firefly algorithm. The firefly algorithm is used in this study to obtain optimal Kp, Ki, and Kd values. The results showed that the firefly algorithm achieved better performance compared to the Ziegler-Nichols method.

Manajemen rumah sakit perlu melakukan monitoring dan pengukuran secara terus-menerus terhadap kinerja layanan pasien untuk memastikan ketercapaian tujuan yang telah ditetapkan. Proses monitoring kinerja memerlukan data dan informasi yang didapatkan dari umpan balik pasien terhadap layanan dokter dan perawat. Hasil monitoring kinerja selanjutnya akan disampaikan kepada pihak-pihak yang berkepentingan, secara efisien dan efektif. Performance Dashboard merupakan alat untuk menyajikan informasi secara sekilas . Dashboard menginformasikan Key Performance Indicators (KPI) dengan menggunakan media penyajian yang efektif. KPI yang digunakan dalam pembangunan Dasboard adalah kuesioner tingkat kepuasan layanan pasien terhadap dokter dan perawat. Penelitian ini lebih menitikberatkan bagaimana rancangan dashboard ini bisa memberikan kemudahan informasi terhadap manajemen rumah sakit untuk memonitor dan mengevaluasi capaian kinerja layanan pasien.

Visi misi tahun 2020 untuk menjadi perguruan tinggi kelas dunia membutuhkan upaya nyatadari jajaran manajemen Universitas Stikubank untuk mewujudkanya, oleh karena itu perludiciptakan suatu kerjasama dari semua tingkat manajemen, dan stakeholder yang ada. Modelmanajemen kualitas Malcolm Baldrige Education Criteria for Performance Excellence(MBCfPE) mempunyai konsep dan prosedur yang menetapkan petunjuk dan kriteria yangmembantu perusahaan dalam mengevaluasi aktivitas perbaikan kualitas. Melalui pengukurankinerja dengan model MBCfPE ini, Universitas akan bisa melihat tingkat pencapaian di dalamproses menuju perguruan tinggi kelas dunia. Pengembangan sebuah model pre assessment bagiUniversitas diharapkan dapat membantu memberikan sebuah gambaran awal sejauh manakesiapan Universitas dalam menerapkan manajemen kinerja kelas dunia. Hasil olah datamenunjukkan bahwa manajemen kinerja Universitas secara umum hasilnya cukup baik. Hal ini ditunjukkan dengan nilai score sebesar 668,55 dari skala 1000. Nilai ini menunjukkan bahwa upayayang dilakukan oleh manajemen Universitas untuk memberikan jaminan mutu pada masyarakatsudah cukup baik. Skor tersebut juga menunjukkan bahwa pendekatan tata kelola yang dilakukanUniversitas cukup sistematis, efektif, dan responsive. Hasil-hasil yang mengacu padapersyaratan-persyaratan mahasiswa dan stakeholder lainya, pasar, dan proses operasional,menunjukkan kinerja dan keunggulan yang baik. Untuk perbaikan kinerja Universitas perludibuat solusi khusus untuk implementasi peningkatan kinerja dari tiap-tiap kategori yangdirasakan kurang nilainya. Sementara itu untuk kategori yang nilainya sudah cukup baik masihperlu untuk terus ditingkatkan. Universitas perlu membangun sistem manajemen kinerja yanglebih baik kedepanya agar visi misi dan tujuan-tujuan strategis dimasa yang akan datang bisatercapai secara efektif dan eisien.

Sistem pendukung keputusan (SPK) untuk performance appraisal pengajuan pinjaman pada koperasi Amanah Sejahtera adalah suatu sistem informasi berbasis komputer yang dapat digunakan sebagai alat bantu bagi manajer koperasi untuk memutuskan diterima atau tidaknya pengajuan pinjaman yang diajukan oleh anggota. Pada sistem pendukung keputusan ini menggunakan prinsip penilaian yang disebut 5C. Prinsip 5C terdiri dari character, capacity, capital, collateral, dan condition. Prinsip 5C akan menjadi kriteria untuk pertimbangan pemberian kredit. Sedangkan preferensi untuk pembobotan pada kriteria tersebut menggunakan analytical hierarchy process. Namun demikian SPK ini juga dapat mengatur kriteria untuk dapat digunakan kriteria, sub kriteria dan susb-sub kriteria yang berbasis kearifan lokal. Secara keseluruhan proses yang ada pada sistem pendukung keputusan pemberian kredit ini adalah manajer menetapkan kriteria, sub kriteria, sub-sub kriteria beserta skornya. Kemudian manajer menginputkan preferensi kriteria antara 1 s.d 9 untuk menghasilkan bobot kriteria. Data yang sudah diinput oleh manajer digunakan staff koperasi untuk melakukan penilaian sehingga dapat dihasilkan keputusan diterima atau ditolak pinjaman yang diajukan atas pinjaman anggota. Hasil pengujian terhadap sistem pendukung keputusan pemberian kredit menunjukan bahwa alternatif keputusan yang dihasilkan sesuai dengan aturan yang ditetapkan.   Kata kunci : performance appraisal, keputusan, pinjaman

Program studi perlu melakukan monitoring dan pengukuran secara terus-menerus  terhadap kinerjanya untuk memastikan ketercapaian tujuan yang telah ditetapkan.  Proses monitoring kinerja memerlukan data dan informasi yang diambil dari  seluruh bagian organisasi. Hasil monitoring kinerja selanjutnya akan disampaikan kepada pihak-pihak yang berkepentingan, secara efisien dan efektif. Performance Dashboard merupakan alat untuk menyajikan informasi secara sekilas . Dashboard  menginformasikan  Key Performance Indicators (KPI) dengan menggunakan media penyajian yang efektif. KPI yang digunakan dalam pembangunan Performance Dasboard  program studi ini seluruhnya menggunakan instrumen dari Badan Akreditasi Nasional (BAN) perguruan tinggi. Metodologi yang digunakan dapat diterapkan dengan baik pada studi kasus mengenai pembangunan dashboard untuk menunjang upaya penjaminan mutu program studi di lingkungan Universitas Stikubank Semarang. Penelitian ini lebih menitikberatkan bagaimana  aplikasi ini bisa memberikan kemudahan informasi terhadap  pengelola program studi, fakultas maupun pihak universitas terhadap capaian mutu dari program studi sarjana. Kata kunci : Performance Dashboard, Key Performance Indikator, Badan Akreditasi Nasional (BAN) Perguruan Tinggi.