Publication Search

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

Traditional association rule analysis is effective at uncovering co-purchase patterns but fails to provide a global structural view of the market, which often results in fragmented and isolated insights. This study proposes a hybrid framework that integrates the Apriori algorithm with a Minimum Spanning Tree (MST) in order to validate and contextualize association rules within a single structural backbone. Transaction data from a retail store are transformed into a weighted, undirected product graph using an inverse-support function, and an MST is then extracted to represent the market backbone, while frequent itemsets and strong rules are obtained using Apriori. Experimental results on 236 multi-item transactions show that the MST backbone comprises 10 products and 9 fundamental links, with 66.67% of these links being confirmed by strong association rules, indicating a substantial coherence between statistical and structural evidence. The proposed model identifies 41 Apriori patterns that can be embedded in the MST and ranks them using a new metric, Structural Distance, which enables the categorization of Core Patterns, Bridge Patterns, and Complex Patterns according to their structural tightness. This hybrid perspective distinguishes dense, strategically meaningful bundles from anomalous but frequent combinations that are structurally peripheral, thereby offering a more holistic and actionable alternative to conventional Market Basket Analysis. The validated framework can support various applications, including store layout optimization, cross-selling strategies, and the design of path-based recommender systems, and it opens avenues for future extensions based on dynamic graphs and Graph Neural Networks.

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.

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.

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.

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.

Information technology is currently developing and growing rapidly in all fields, supported by the advancement of computer technology. The application of information systems can also be applied to the financial services sector, including cooperatives like K.S.P Al Hudori. The loan service system remains ineffective due to the manual data management process, which involves writing data into ledgers, which are easily lost or damaged due to the paper-based nature of the data. This research aims to implement a loan information system at K.S.P Al Hudori that can assist with loan data verification, search, and report generation. This information system was designed using Embarcadero XE2 and Microsoft Access 2007 as its database. This system has been implemented at KSP Al Hudori. It is hoped that this information system will simplify the loan management process at K.S.P Al Hudori

The advancement of information technology has had a significant impact across various sectors, including healthcare. The digitalization of healthcare services has become a solution to improve efficiency, effectiveness, and accessibility for the public. Puskesmas Selindung still uses a manual patient registration system, which leads to several issues such as long queues, extended waiting times, and the risk of lost or damaged patient records. Based on visit data, the number of patients coming to Puskesmas Selindung has increased each year. Therefore, a digital queue system is needed to optimize the patient registration process. This research aims to analyze and design an Android-based patient registration queue application to improve service efficiency at Puskesmas Selindung. The research methods include system requirements analysis, user interface design, and the development of core features to support the online patient registration process. The implementation of this application is expected to reduce long queues, speed up administrative processes, and make it easier for patients to access healthcare services more effectively and accurately. With this Android-based system, the quality of healthcare services at Puskesmas Selindung is expected to improve significantly.

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.

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.

Information technology is currently developing and growing rapidly in all fields, supported by the advancement of computer technology. The application of information systems can also be applied to the financial services sector, including cooperatives like K.S.P Al Hudori. The loan service system remains ineffective due to the manual data management process, which involves writing data into ledgers, which are easily lost or damaged due to the paper-based nature of the data. This research aims to implement a loan information system at K.S.P Al Hudori that can assist with loan data verification, search, and report generation. This information system was designed using Embarcadero XE2 and Microsoft Access 2007 as its database. This system has been implemented at KSP Al Hudori. It is hoped that this information system will simplify the loan management process at K.S.P Al Hudori.

Barru Regency tourism has a variety of tourist attractions, but information related to the location and potential of tourism has not been digitally integrated, making it difficult for tourists and the government to access data. This study aims to design and build a web-based Geographic Information System (GIS) that can map the location of tourist attractions in Barru Regency interactively, easily accessible, and equipped with supporting information in the form of descriptions, types of tourism, photos, and travel routes. The research method used is Research and Development (R&D) with the stages of tourist attraction surveys, interface design, feature development, and system testing. The results of the study are in the form of a website "Web-Based Geographic Information System for Mapping Tourist Attractions in Barru Regency" which is able to present tourist information systematically and easily understood. The conclusion of this study shows that the developed system can be a supporting medium in disseminating tourism information, helping tourists find tourist locations, and supporting the local government in managing and developing the tourism sector based on spatial data. This application also has the potential to be an educational and promotional tool to increase tourist visits to Barru Regency

Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan yang dapat membantu tim Marketing Officer (MO) PT. Alvarel Technology Innovation dalam menentukan status pelanggan secara objektif dan terstruktur. Sistem ini dirancang menggunakan kombinasi metode Analytical Hierarchy Process (AHP) dan Weighted Sum Model (WSM). Metode AHP digunakan untuk menentukan bobot kriteria yang meliputi Potensial Pasar, Urgensi, Finansial, serta Hubungan dan Reputasi, dengan memastikan konsistensi matriks perbandingan berpasangan. Hasil pembobotan kemudian digunakan dalam metode WSM untuk melakukan perhitungan skor total pelanggan dan menyusun pemeringkatan status berdasarkan nilai tertinggi hingga terendah. Data penelitian diperoleh dari catatan internal perusahaan dan wawancara dengan Marketing Officer, dengan jumlah sampel 30 pelanggan. Hasil pengujian menunjukkan bahwa sistem dapat menghasilkan peringkat status pelanggan dalam lima kategori, yaitu potensial, prospek, pending, pasif, dan skip. Temuan utama memperlihatkan bahwa kategori prospek memperoleh skor tertinggi dan menjadi prioritas tindak lanjut. Dengan demikian, sistem pendukung keputusan berbasis AHP–WSM ini mampu mengurangi subjektivitas, meningkatkan efisiensi, serta memberikan rekomendasi yang lebih akurat dan terukur untuk mendukung pengambilan keputusan strategis perusahaan dalam pengelolaan pelanggan.

Improving the accuracy of emission monitoring in the Continuous Emission Monitoring System (CEMS) is crucial to support compliance with environmental regulations, especially in gas and steam-based power plants (PLTGU). At PT Jawa Satu Power's PLTGU, the purging system on the CEMS sample probe is still performed manually and limited to scheduled preventive maintenance, potentially reducing emission data reliability due to particle contamination. This study aims to design and simulate an automatic purging system based on a Programmable Logic Controller (PLC), taking into account technical parameters such as timer, gas pressure, temperature, and sample flow rate. The system design was carried out through the modeling of automatic control logic using CX-Programmer software, with a protection approach based on real-time conditions and timing. The design results show that the automatic purging system can improve cleaning consistency, reduce the risk of contamination, and enhance the integrity of emission monitoring data. This study is expected to serve as a foundation for developing a more applicable and integrated automated purging system for CEMS in the future.

Digital transformation in government bureaucracy has become a strategic step to improve efficiency, accountability, and transparency, including in the management of regional legal products. In the Regional Secretariat of Tegal Regency, the process of submitting legal products is still done manually, which causes inefficiencies, delays, and a lack of document traceability. To address these issues, a web-based legal product submission application has been developed with document tracking features. This research uses the Waterfall system development method, and implements a FIFO (First In First Out) queuing system in the submission process, along with Role-Based Access Control (RBAC) for managing user access rights. The goal of this system is to create a faster, more transparent, and digitally documented application process. The implementation results show that the application is able to systematically and integratively manage the flow of submissions, corrections, verification, and the ratification of legal products. Features such as a Login page with CAPTCHA, analysis dashboards, tracking, and monthly reports enhance the monitoring and security functions of the system. This application can be an effective solution in supporting the digitization of regional legal bureaucracy, as well as providing ease and efficiency for regional officials in preparing and submitting drafts of legal products digitally.

The increasing need for electrical energy in Indonesia encourages the development of new renewable energy (EBT), including hydroelectric power plants (PLTA) as an environmentally friendly solution. This research was conducted at the Mentawa Dam, West Toili District, Banggai Regency, Central Sulawesi, to analyze the potential of water energy that can be utilized as a source of micro-hydro power plants (PLTMH). This dam currently functions as irrigation and a tourist attraction, but has significant potential to generate sustainable electrical energy. The research methods include measuring water discharge using the float method, measuring the height of the water fall (head) with an altimeter and GPS, and analyzing the potential for electrical power using the formula P = η ⋅ ρ ⋅ g ⋅ Q ⋅ H. The measurement results show an average water discharge of 3.01 m³ / s and a water fall (head) of 6.56 meters. With a turbine efficiency of 80%, the potential electrical power that can be generated reaches 154.76 kW. This study recommends the use of Kaplan turbines in Mentawa Dam, which are ideal for large discharge conditions and low to medium heads, and can adapt to discharge fluctuations, making it an optimal choice to maximize energy potential. This research is expected to provide sustainable energy solutions for the people of Toili Barat District and support the development of the tourism sector.

State Senior High School 7 Surakarta is one of the public schools located in Surakarta City. The library activities, including member data management, book processing, and book borrowing and returning, are still conducted manually using physical logbooks. This manual process is considered inefficient and prone to errors. The purpose of this study is to develop a book borrowing system at State Senior High School 7 Surakarta that serves as a tool to assist officers in recording, loans, and returning books. This system is designed using the CodeIgniter framework to support WEB displays, programming in PHP, and using MySQL for database management. This system is created using the System Development Life Cycle (SDLC) method with a waterfall model that includes the stages of analysis, design, implementation, testing, and maintenance. System testing was conducted using Black-Box Testing and the System Usability Scale (SUS). The Black-Box Testing results showed that all features and functions operated correctly. The SUS evaluation produced a score of 75.68%, indicating that users generally agreed with the implementation of the system, which falls under the "acceptable" classification.

Due to the busy activities outside the home, some people do not have time or forget to water their plants, so when they return home they find that the plants have died from drought. Therefore, to overcome this problem by conducting research using Internet of Things technology. The aim of this research is to design an automatic watering tool to overcome manual watering and support learning. This prototype uses a NodeMCU ESP8266 as the main controller, a soil moisture sensor is used to read soil moisture.  Soil moisture sensors are used to detect soil moisture. If the soil humidity is below the minimum limit, the watering process will be active and the watering process will be active if the NodeMCU ESP8266 receives commands from the smartphone.

The election of the OSIS chairman at SMK Al-Ikhlas Losari often influences subjectivity, so a goal-oriented system is needed to select the best candidate. This study applies the WASPAS method in a Decision Support System to broadcast OSIS chairman candidates based on criteria such as attendance, integrity, academic achievement, and health. Data were collected through observation, interviews, and literature studies, then processed using Microsoft Excel. The results of the WASPAS calculation yield a Qi preference value, which determines the ranking of candidates transparently. This system reduces subjectivity, increases efficiency, and ensures the selection of competent leaders, supporting the OSIS vision.