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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Voice Of Informatics MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Informatika Global CCIT (Creative Communication and Innovative Technology) Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics Systematics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi INFORMASI (Jurnal Informatika dan Sistem Informasi) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Abdimasku : Jurnal Pengabdian Masyarakat Infotek : Jurnal Informatika dan Teknologi Jurnal Teknologi Informatika dan Komputer SKANIKA: Sistem Komputer dan Teknik Informatika Journal of Innovation Research and Knowledge Jurnal Pengabdian Mandiri Literasi: Jurnal Pengabdian Masyarakat dan Inovasi SATIN - Sains dan Teknologi Informasi Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi)
Tri Sugihartono
Institut Sains Dan Bisnis Atma Luhur
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Nursing Social Sciences Other

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A Hybrid SEM-PLS and ANN Approach for Predicting Student Loyalty in Higher Education Learning Management Systems Hamidah; Sarwindah; Hengki; Tri Sugihartono
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1625

Abstract

This study aims to develop a hybrid Structural Equation Modeling–Partial Least Squares (SEM-PLS) and Artificial Neural Network (ANN) approach to analyze student loyalty in Learning Management Systems (LMS) at ISB Atma Luhur. Data were collected from 200 students at ISB Atma Luhur, representing a single-institution sample, and analyzed using SEM-PLS to examine causal relationships and ANN (Multilayer Perceptron) implemented in SPSS to support predictive analysis. The model includes e-service quality, user experience, information quality, and system quality as predictors of satisfaction and loyalty. The SEM-PLS results show that E-Service Quality (β = 0.350), System Quality (β = 0.170), and User Experience (β = 0.292) significantly affect Satisfaction, whereas Information Quality is not statistically significant (p = 0.054). Satisfaction positively influences Loyalty (β = 0.360), and User Experience has the strongest direct effect on Loyalty (β = 0.484). The model explains a substantial proportion of variance (R² = 0.717 and 0.631) with positive Q² values (0.460 and 0.379). Across ten independent runs, the ANN model achieved an average accuracy of 84.88% (SD = 2.82) and an average AUC of 0.949 (SD = 0.003), indicating stable predictive performance, indicating promising predictive performance under the current testing configuration. The findings provide context-specific explanatory and predictive insights into student loyalty in LMS, however, they should be interpreted with caution due to discriminant-validity limitations and the single-institution setting of the study.
SISTEM INFORMASI PEMESANAN ONLINE PUDING S DAN ES DRINK MENGGUNAKAN METODE FAST Estefania; Serlie Natalia; Estefania Estefania; Tri Sugihartono
Journal of Innovation Research and Knowledge Vol. 5 No. 9 (2026): Februari 2026
Publisher : Bajang Institute

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Abstract

Puding S dan Es Drink merupakan usaha kecil menengah yang masih menggunakan sistem pencatatan manual dengan nota kertas dalam mengelola pemesanan. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi pemesanan online menggunakan metode FAST yang dapat menggantikan sistem manual. Metode FAST dipilih karena menawarkan pendekatan sistematis dan terstruktur yang sesuai dengan kebutuhan usaha kecil. Penelitian ini melaksanakan tujuh fase pengembangan sistem meliputi perencanaan cakupan, analisis masalah, analisis kebutuhan, perancangan logis, perancangan fisik, konstruksi dan pengujian, serta instalasi dan pengiriman. Sistem yang dikembangkan memiliki fitur menampilkan sepuluh varian rasa puding, keranjang belanja, formulir pemesanan, pengelolaan status pesanan, dan pelaporan penjualan. Hasil penelitian menunjukkan bahwa sistem informasi pemesanan online meningkatkan efisiensi operasional, mengurangi kesalahan pencatatan, memperluas jangkauan pasar, dan meningkatkan kepuasan pelanggan. Sistem ini dirancang dengan skalabilitas yang baik sehingga dapat dikembangkan lebih lanjut di masa depan sesuai dengan pertumbuhan bisnis
Implementing Random Forest for Eye State Detection in an EEG-Based Brain-Computer Interface System Muhammad Alfathan; Tri Sugihartono
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16054

Abstract

Eye state detection using Electroencephalogram (EEG) signals is a growing research area in Brain-Computer Interface (BCI) systems, with practical implications for drowsiness monitoring and assistive technologies. However, EEG signals are highly susceptible to extreme outliers caused by muscle artifacts and electrode interference, which significantly degrade model performance when left unaddressed. Previous studies have largely overlooked explicit outlier handling strategies and often rely solely on accuracy as the evaluation metric, which is insufficient for imbalanced class distributions. This study aims to implement the Interquartile Range (IQR) Clipping method for outlier handling on EEG signals and develop a Random Forest classification model to distinguish open-eye and closed-eye states, evaluated through seven comprehensive metrics. The EEG-Eye-State dataset from the UCI Machine Learning Repository, consisting of 14,980 samples across 14 EEG sensor features, was used. IQR Clipping with bounds [Q1 − 1.5×IQR, Q3 + 1.5×IQR] was applied to all sensors, followed by StandardScaler normalization and an 80:20 Stratified Train-Test Split. A Random Forest model with 100 estimators and balanced class weights was trained and validated using Stratified 10-Fold Cross-Validation. IQR Clipping successfully handled 12,737 outlier instances across all sensors without discarding any samples. The model achieved an accuracy of 92.49%, Balanced Accuracy of 92.14%, ROC-AUC of 0.9791, PR-AUC of 0.9759, F1-Score Macro of 0.9236, Matthews Correlation Coefficient (MCC) of 0.8486, and Cohen Kappa of 0.8474. Cross-validation confirmed model stability with a mean accuracy of 92.86% ± 0.36% and ROC-AUC of 0.9809 ± 0.0020. Feature importance analysis identified sensors O1 (11.81%), P7 (10.59%), and F7 (10.15%) as the most dominant contributors. These results confirm that combining IQR Clipping with Random Forest produces a stable, accurate, and neuroanatomically interpretable model for EEG-based eye state classification, offering a strong foundation for real-world BCI and driver drowsiness detection systems.
Multi-Metric Evaluation of Machine Learning Algorithms for Diabetes Prediction Using Feature Importance and ROC Analysis Fendi Setiawan; Tri Sugihartono
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16133

Abstract

Diabetes mellitus has become a major global health threat, and many undiagnosed cases remain undetected due to some limitations of the conventional diagnostic methods. Despite the promising results of machine learning (ML) for early diabetes diagnosis, the majority of the current research assessing algorithms either uses insufficient metrics or does not follow a consistent assessment approach. This paper addresses that gap by utilising an integrated evaluation framework. The framework includes feature importance analysis, Pearson correlation assessment, confusion matrix decomposition, and ROC-AUC comparison. It applies this framework to the Pima Indians Diabetes Dataset (mde) and four popular ML classification algorithms: Naive Bayes, Decision Tree, Random Forest, and Logistic Regression. The most significant predictors, according to our feature analysis, were glucose (27.6%), body mass index (16.0%), age (12.7%), and diabetes pedigree function (12.7%). Among the classifiers, Random Forest exhibited the greatest accuracy (76.0%) and precision (68.1%), Naive Bayes the best recall (64.8%), and Logistic Regression the highest AUC-ROC (82.3%). For patients at high risk, the models' virtual projections across all three risk profiles were in agreement. Model selection should be determined by the unique clinical screening aim, since these findings suggest that there is no one better universal method. Random Forest and Logistic Regression are the most promising for assisting in preliminary diabetes prediction, although further validation on diversity datasets is needed prior to clinical deployment.
Peranan Strategis Teknologi dalam Memperkuat Wawasan Kebangsaan dan Karakter Generasi Muda untuk Bela Negara di Era Digital Tri Sugihartono; Syafrul Irawadi; Rahmat Sulaiman; Elly Yanuarti; Agustina Mardeka Raya; Goenawan Brotosaputro
Jurnal Pengabdian Masyarakat Berbasis Teknologi Vol 7 No 01 (2026): Volume 7, Nomor 1, Mei 2026
Publisher : ISB Atma Luhur

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Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk menguatkan peranan strategis teknologi dalam membentuk wawasan kebangsaan, karakter, dan kesadaran bela negara generasi muda di era digital. Dilatarbelakangi oleh perubahan global, bonus demografi menuju Indonesia Emas 2045, serta ancaman digital seperti hoaks, judi online, radikalisme, dan disinformasi, kegiatan ini dilaksanakan di Kampus ISB Atmaluhur Belinyu. Metode yang digunakan adalah ceramah interaktif, diskusi kasus, dan tanya jawab. Peserta berjumlah 36 orang yang terdiri dari pelajar SMA/SMK dan mahasiswa. Hasil kegiatan menunjukkan peningkatan signifikan dalam pemahaman peserta tentang literasi digital berbasis Pancasila, pentingnya wawasan kebangsaan, serta bentuk-bentuk bela negara non-militer. Kegiatan ini direkomendasikan untuk menjadi program berkelanjutan dalam membangun generasi unggul yang berkarakter dan berdaya saing global.
Comparative Sentiment Analysis of Profits Anywhere Application Reviews Using KNN and Naïve Bayes Yurindra; Chandra Kirana; Dian Novianto; Tri Sugihartono; Muhammad Ilham
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1658

Abstract

User reviews on Google Play Store provide valuable information regarding application service quality and user satisfaction. This study aims to compare the performance of the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms for sentiment analysis of reviews of the Profits Anywhere application, a digital business platform offering affiliate marketing and financial services. A total of 1,037 reviews were collected through web scraping and subsequently underwent data cleaning and validation, resulting in 957 valid reviews used in the analysis, consisting of 674 positive and 283 negative reviews. The dataset was subjected to a comprehensive text preprocessing pipeline, including text cleaning, case folding, tokenization, stopword removal, and stemming, followed by feature representation using the Term Frequency–Inverse Document Frequency (TF-IDF) technique. The data were partitioned using an 80:20 stratified train–test split, while hyperparameter optimization was conducted using GridSearchCV with 5-fold stratified cross-validation. Experimental results demonstrate that KNN outperformed Naïve Bayes on the evaluated dataset and experimental configuration, achieving an accuracy of 92.19%, weighted precision of 92.23%, weighted recall of 92.19%, and weighted F1-score of 92.21%. In contrast, Naïve Bayes achieved an accuracy of 88.54%, weighted precision of 89.04%, weighted recall of 88.54%, and weighted F1-score of 88.70%. These findings provide empirical evidence regarding algorithm selection for sentiment analysis of application reviews in the fintech and digital business domains.
Co-Authors Agustina Mardeka Raya Agustina Mardeka Raya Agustina Merdeka Raya Ahmad Faqih Alkayess Al-Fajri, M. Hikmal Ari Amir Alkodri Balqis, Iqlima Benny Wijaya Chandra Kirana Chrisna Putra, Rendy Rian Delpiah Wahyuningsih Dian Novianto Dian Novianto Djoko Soetarno Dwi Sandro, Irsad Elly Yanuarti Elly Yanuarti ELLYA HELMUD Ellya Helmud Estefania Estefania Estefania Fajriansyah Fajriansyah Fendi Setiawan Fernandy Ricardo Antonius Fitriyani Fransiskus Panca Juniawan Goenawan Brotosaputro Hamidah Hengki Irfan, Khoiril Jesi Safitri Juwita, Ayu Ratna Marini marini Marini Marini Marini Marsela, Rania Maxrizal Muhammad Alfathan Muhammad Ilham Puad Nabil Hakiki Rachmatsyah, Agus Dendi Rahmat Sulaiman Rahmat Sulaiman Rahmat Tomi Setiaji Revalina, Revalina Royhan Indallah Sarwindah Sarwindah Sarwindah Sarwindah . Sarwindah Sarwindah Serlie Natalia Sri Jansiska Supardi Syafrul Irawadi Yurindra Yurindra