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All Journal JSAI (Journal Scientific and Applied Informatics) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Sistem Informasi dan Teknologi Informasi
Cahyono Budy Santoso
Universitas Pembangunan Jaya
Computer Science & IT Decision Sciences, Operations Research & Management Education

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Aplikasi Pengelolaan Acara dan Informasi Kampus Cahyono Budy Santoso; Maria Rachel Kesya Makarena; Anezza Nuraina; Firah Dzahabiyyah; Sahla Lutfiah Bilqis
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 9 No. 1 (2025): Volume 9 Nomor 1 Januari 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v9i1.14378

Abstract

Pengelolaan acara di lingkungan kampus sering kali menghadapi tantangan terkait koordinasi, penyebaran informasi, dan keterlibatan mahasiswa. Untuk menjawab permasalahan ini, Universitas Pembangunan Jaya (UPJ) mengembangkan JayaEvents, sebuah aplikasi digital yang dirancang untuk mempermudah perencanaan, pelaksanaan, dan promosi acara kampus. Aplikasi ini mengintegrasikan berbagai fitur, seperti pendaftaran acara, pengelolaan tiket, notifikasi, dan forum diskusi, yang bertujuan untuk meningkatkan keterlibatan mahasiswa serta memfasilitasi koordinasi antar pihak yang terlibat dalam acara. Penelitian ini bertujuan untuk mengevaluasi efektivitas JayaEvents dalam meningkatkan pengelolaan acara kampus, mempermudah akses informasi, serta meningkatkan partisipasi mahasiswa dalam berbagai kegiatan. Penelitian ini menggunakan metode kualitatif dengan pendekatan studi kasus yang mengkaji pengalaman pengguna aplikasi, efektivitas fitur-fitur utama, dan dampaknya terhadap keterlibatan mahasiswa. Hasil penelitian diharapkan memberikan wawasan tentang penerapan aplikasi manajemen acara dalam konteks universitas, serta memberikan rekomendasi untuk pengembangan lebih lanjut JayaEvents agar lebih efektif dan efisien dalam mendukung kegiatan kampus.
Implementasi NLP Klasifikasi Berita Pemilu Menggunakan Algoritma LSTM Harry Vadilan Sianturi; Cahyono Budy Santoso
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8212

Abstract

This study examined the application of a Long Short-Term Memory (LSTM)-based text classification method to categorize election news according to presidential and vice-presidential candidate entities. The core problem addressed was the lack of an automated classification system capable of identifying political affiliations directly within the vast volume of digital news content. In this research, news data were collected from open-access sources and automatically labeled based on the occurrence of candidate-related keywords. A supervised learning approach was implemented using the LSTM architecture to capture sequential patterns within the news text. The evaluation results demonstrated that the model achieved a validation accuracy of 95.44% and a macro-averaged F1-score of 0.95, indicating strong classification performance across all candidate categories. Furthermore, predictions on test data revealed the model’s consistency and stability in recognizing political entities. This study confirmed the effectiveness of the LSTM-based approach for entity-based election news classification and highlighted its potential for integration into automated media analytics and political discourse monitoring systems.
Perancangan Aplikasi Sistem Presensi Guru Berbasis Web Menggunakan Geo Fencing Pada Sekolah SDN XYZPerancangan Aplikasi Sistem Presensi Guru Berbasis Web Menggunakan Geo Fencing Pada Sekolah SDN XYZ Faizul Anwar Ramdhani; Cahyono Budy Santoso
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8231

Abstract

The web-based teacher attendance system application with Geo-Fencing technology integration is designed to improve the accuracy and efficiency of the teacher attendance recording process in the school environment. The use of Geo-Fencing allows attendance to only be done when the user is in a predetermined area, thus minimizing the potential for fraud and data manipulation. This research aims to develop a web-based attendance application and evaluate its usability level using the System Usability Scale (SUS) method. The evaluation was conducted on 20 respondents consisting of teachers and school administrators. Based on the test results, an average SUS score of 75 was obtained, which is included in the Good usability category. Thus, this application is considered quite easy to use, effective, and acceptable to users. The results of this study indicate that the web-based teacher attendance system application with Geo-Fencing has the potential to be widely implemented in the school environment, with some further development recommendations to improve user experience.
Evaluasi Metode Retrieval pada Chatbot Domain Khusus Berbasis Retrieval-Augmented Generation Asmaidin Asmaidin; Cahyono Budy Santoso
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9897

Abstract

This study evaluated retrieval methods in the implementation of a domain-specific chatbot based on Retrieval-Augmented Generation to improve information accuracy and relevance while reducing hallucination risks. The primary problem addressed was the incorrect selection and prioritization of contextual documents in chatbot systems built on large language models, particularly in technical domains. An experimental approach was applied by comparing three retrieval strategies: lexical retrieval based on term frequency–inverse document frequency, semantic retrieval using vector representations, and a hybrid retrieval method combining lexical and semantic signals. System performance was measured using Recall at different ranking thresholds and Mean Reciprocal Rank to assess both document discovery and ranking quality. The results demonstrated that lexical retrieval achieved the highest precision at the top-ranked position, while semantic retrieval showed reduced effectiveness due to semantic drift in technical documents. The hybrid approach improved mid-range recall performance but still exhibited ranking ambiguity for top-ranked results. These findings indicated that retrieval quality in Retrieval-Augmented Generation systems depended more on effective ranking and context prioritization than on document availability alone. The study concluded that systematic evaluation of retrieval methods was essential for developing reliable domain-specific chatbots.
Pengembangan Sistem Media Intelligence ESG Berbasis NLP Bahasa Indonesia Menggunakan TF-IDF dan IndoBERT Lukman Hakim Moeslich; Cahyono Budy Santoso
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10586

Abstract

Monitoring Environmental, Social, and Governance (ESG) issues in Indonesia’s nickel mining industry has become increasingly important due to growing demands for transparency and sustainability. However, automated ESG media analysis for Indonesian-language news remains limited. This study aims to develop an ESG media intelligence system based on Natural Language Processing (NLP) to analyze media perception toward PT Indonesia Weda Bay Industrial Park (IWIP) and PT Weda Bay Nickel (WBN). The proposed system employs an eight-stage pipeline consisting of automated news collection, Indonesian text preprocessing, ontology-based ESG labeling, text classification using TF-IDF + LinearSVC and IndoBERT, as well as sentiment and ESG risk trend analysis. A total of 1,693 news articles published between January 2020 and May 2026 were collected, with 1,320 articles successfully labeled using an ontology-based weak supervision approach. Experimental results show that the best TF-IDF configuration achieved a Macro-F1 score of 0.7693, while IndoBERT achieved 0.7698. The findings indicate that TF-IDF remains competitive with transformer-based models on limited Indonesian ESG datasets. Media analysis revealed that IWIP received predominantly negative media perception on environmental and social issues, while WBN showed relatively more positive governance-related coverage. This research contributes to the development of Indonesian-language ESG media intelligence for the mining industry.
Perbandingan K-Means, Hierarchical Clustering Dan K-Medoids Untuk Segmentasi Pasar Berdasarkan Evaluasi Silhouette Score Denny Ganjar Purnama; Safrizal; Cahyono Budy Santoso
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10849

Abstract

This study compares the performance of K-Means, Agglomerative Hierarchical Clustering, and K-Medoids algorithms for market segmentation using PT XYZ sales data. The dataset consists of the Quantity and Expected Revenue attributes and was processed through data cleaning, currency-to-numeric conversion, invalid data removal, logarithmic transformation, and standardization, resulting in 923 valid records. Clustering was performed using three clusters, representing Low Value, Mid Value, and High Value customer segments. Performance was evaluated using the Silhouette Score, where K-Means achieved 0.4805, Agglomerative Hierarchical Clustering 0.4808, and K-Medoids 0.4840. Although the performance differences among the algorithms were relatively small, K-Medoids achieved the highest score and was therefore selected as the final model. The resulting segmentation consisted of 188 Low Value customers (20.37%), 469 Mid Value customers (50.81%), and 266 High Value customers (28.82%). These findings indicate that K-Medoids provides the best clustering quality while offering greater interpretability through medoid-based cluster centers representing actual data objects. The proposed segmentation can support companies in developing differentiated marketing strategies for low-, medium-, and high-value customer segments.
Diabetes Risk Prediction Using Machine Learning Methods: A Case Study of Logistic Regression, Random Forest, and Support Vector Machine Putri Ines Salsabilla; Alicia Stifa Nur Sahra; Davina Intan Aurelya; Cahyono Budy Santoso
Jurnal Sistem Informasi dan Teknologi Informasi Vol. 3 No. 2 (2025): June 2026
Publisher : LP2M Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/justinfo.v3i2.3474

Abstract

Diabetes is a global health problem whose prevalence continues to increase and has the potential to cause serious complications if not detected early. Early detection of diabetes risk is very important to prevent fatal consequences and support more effective clinical decision-making. This study aims to analyze and compare the performance of three machine learning algorithms, namely Logistic Regression, Random Forest, and Support Vector Machine, in predicting diabetes risk based on patient symptom and health condition data. The methods used include data collection from open sources, data pre-processing with imputation and normalization, model training using the three algorithms, and model performance evaluation based on accuracy, precision, recall, and F1-score metrics. The dataset used consisted of 520 samples with 17 attributes, including main symptoms such as frequent urination, excessive thirst, and demographic factors. The results of the study show that the Random Forest algorithm has the best performance with an accuracy of 99%, precision of 1, recall of 0.99, and F1-score of 0.99, surpassing Logistic Regression and Support Vector Machine. These findings confirm that Random Forest is very effective in capturing the complexity of clinical data and can be relied upon as a tool for early detection of diabetes risk. This research contributes to the development of decision support systems in the field of digital health by offering an accurate and efficient machine learning-based prediction approach.
Co-Authors Alicia Stifa Nur Sahra Anezza Nuraina Asmaidin Asmaidin Davina Intan Aurelya Denny Ganjar Purnama Faizul Anwar Ramdhani Firah Dzahabiyyah Harry Vadilan Sianturi Lukman Hakim Moeslich Maria Rachel Kesya Makarena Putri Ines Salsabilla Safrizal Safrizal Sahla Lutfiah Bilqis