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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Ilmiah Media Sisfo JOURNAL V-TECH (VISION TECHNOLOGY) Account : Jurnal Akuntansi, Keuangan dan Perbankan Jaringan Laboratorium Medis Jurnal Teknik Informatika (JUTIF) Quantitative Economics and Management Studies Journal of Applied Accounting And Business (JAAB) Journal of Trends Economics and Accounting Research Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Ilmiah Manajemen dan Kewirausahaan Modem : Jurnal Informatika dan Sains Teknologi Jurnal Kajian Hukum Dan Kebijakan Publik Prosiding Seminar Nasional Ilmu Teknik Jurnal Serina Abdimas
Gunardi Gunardi
Universitas Dinamika Bangsa
Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences

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SCRUM AND ITIL-BASED SUPPORT SYSTEM DESIGN AND IMPLEMENTATION AT RAPHA THERESIA HOSPITAL Kasrizal Kasrizal; Sharipuddin Sharipuddin; Joni Devitra; Gunardi Gunardi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7004

Abstract

This research addresses the challenges of manual IT service management at Rapha Theresia Hospital, where existing processes lacked systematic tracking and reporting, leading to operational inefficiencies. The purpose was to design and implement a web-based IT support system for systematic documentation of IT requests and repairs, integrating the Scrum agile development methodology with the ITIL framework, and enabling comprehensive IT performance reporting for management evaluation. The study employed a hybrid methodological approach, combining Scrum for iterative development and ITIL for robust service delivery. Research methods included problem identification, and iterative implementation across four sprints with defined Service Level Agreements (SLAs). Rigorous User Acceptance Testing (UAT) validated the system's functionality. Results show successful implementation of a centralized system managing IT requests, assets, and reports, significantly improving operational efficiency, service reliability, and fostering data-driven decision-making. The system enhanced coordination, transparency, and accelerated service resolution within the IT team.
Improving Bioethanol Sentiment Analysis Performance using SMOTE in Machine Learning Model Comparison Rajhu Ilham Pradana; Jasmir Jasmir; Gunardi Gunardi
SISTEMASI Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6300

Abstract

Sentiment analysis of public policies on social media is crucial for government evaluation; however, it is often challenged by highly imbalanced datasets. This study aims to address this issue through a case study on public sentiment toward bioethanol fuel policies on YouTube, where the cleaned dataset after preprocessing consisted of 2,409 comments dominated by negative sentiment (1,430 comments), followed by neutral sentiment (734 comments), and only a small number of positive sentiments (245 comments). The performance of classical Machine Learning (ML) models was severely degraded due to this imbalance, particularly in detecting the minority class. This study applied TF-IDF weighting for feature extraction, followed by the Synthetic Minority Oversampling Technique (SMOTE) to balance the training data (1,927 samples) before comparing the performance of three ML algorithms: Logistic Regression, Support Vector Machine (SVM), and LightGBM. The evaluation results on the testing dataset (482 samples) demonstrate that the implementation of SMOTE significantly improved the models’ ability to recognize the “Positive” class. The LightGBM model combined with SMOTE achieved the best performance, with an accuracy of 64.11%. In particular, the application of SMOTE successfully increased the minority-class F1-score from a baseline of 18.18% to 35.29%. These findings confirm that handling imbalanced data is a critical step in producing valid and reliable sentiment analysis results.
Comparison of Machine Learning Algorithms for Credit Score-based Banking Customer Churn Prediction Suryadillah Hendrawinata; Jasmir Jasmir; Gunardi Gunardi
SISTEMASI Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6148

Abstract

A high customer churn rate represents a significant challenge for the banking industry, leading to substantial financial losses and higher acquisition costs for new customers. Proactively identifying customers who are likely to churn is essential for implementing effective retention strategies. This study aims to address this issue by implementing and comprehensively comparing three different machine learning classification algorithms: Logistic Regression, Random Forest, and XGBoost. The study utilized a secondary dataset consisting of bank customer profiles from 10,000 customers with various characteristics, including credit scores, account balances, and transaction activities. The research methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The models were evaluated using several metrics, including Accuracy, Precision, Recall, F1-Score, and ROC-AUC. The findings indicate that the ensemble models significantly outperformed the linear model (Logistic Regression), which achieved an F1-Score of only 0.286. Random Forest emerged as the best-performing model in this study, achieving the highest Accuracy (0.864), F1-Score (0.590), and ROC-AUC (0.852). In comparison, XGBoost demonstrated competitive performance with an F1-Score of 0.579 and a ROC-AUC of 0.832. The study concludes that Random Forest provides the most optimal overall performance, offering the strongest capability for identifying at-risk customers within the dataset.
Perancangan Sistem Informasi Pengarsipan Digital Data Pelanggan pada PT. Jambi Independent Press Berbasis Web Miranda Miranda; Ronald Naibaho; Gunardi Gunardi
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 4 No. 3 (2026): Juli : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v4i3.923

Abstract

PT. Jambi Independent Press is one of the companies engaged in publishing newspapers in its data processing using Microsoft Excel, but there are still many obstacles in data processing, such as the difficulty of recording customer data archiving data, planning previously planned activities because the data search process is valued slow, data does not appear automatically so you have to input it repeatedly, and data cannot be integrated with each other because there is no database. The purpose of this study is to analyze the system that is currently running, in order to overcome the problems faced at PT. Jambi Independent Press, by designing a Customer Data Digital Archiving Information System Design at PT. Web-Based Jambi Independent Press. The research framework that will be carried out in solving the problems discussed is identifying, conducting information searches based on theoretical foundations, collecting data using observation and interview methods, analyzing to find solutions to problems faced by PT. Jambi Independent Press. The system development method uses the waterfall model, the implementation of this research uses the PHP Programming Language and MySQL DBMS, to produce data processing applications that are expected to facilitate data processing and report generation.
Peran Regulasi Nasional dan Perjanjian Bilateral Dalam Penyelesaian Sengketa Menurut Hukum Bisnis di Indonesia Dwi Sukma Ramdhani; Gunardi Gunardi
Jurnal Kajian Hukum Dan Kebijakan Publik | E-ISSN : 3031-8882 Vol. 3 No. 1 (2025): Juli - Agustus
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62379/qc173786

Abstract

The development of international business must be based on clear legal certainty for investors and business actors in Indonesia. National regulations form the main legal basis, while bilateral agreements provide additional protection for foreign investors. This study aims to analyze the interaction between the national legal framework and international commitments, particularly in the context of bilateral investment agreements. The approach used is normative juridical with literature study and document analysis. The results of the study show that national regulations provide domestic legal certainty, while bilateral agreements strengthen international cooperation and facilitate foreign investment. However, potential conflicts may arise when national regulations are not fully aligned with international agreements, requiring careful legal harmonization. In conclusion, Indonesia needs to strengthen its regulatory framework to be compatible with bilateral agreements in order to create a conducive and competitive business climate..
Perancangan Sistem Informasi Kas Masuk Dan Kas Keluar Berbasis Web Pada Kantor Notaris Krisalia Wahyu Sari Kota Jambi Gunardi; Ghea Permata Rizky
Journal of Applied Accounting And Business Vol. 3 No. 2 (2021): JAAB - Desember 2021
Publisher : LP2M Politeknik Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37338/jaab.v3i2.73

Abstract

Krisalia Wahyu Sari Notary Office is a medium business unit located at Jln. Otto Iskandardinata No.59, which is engaged in making Authentic Deed regarding all deeds, agreements and provisions required by statutory regulations. The purpose of this study is to analyze the current system, so that it can overcome the problems faced at the Krisalia Wahyu Sari Notary Office of Jambi City, by designing Web-Based Cash In and Out Cash Information System Design at the Krisalia Wahyu Sari Notary Office in Jambi City . As for the constraints faced, company data security is not guaranteed, this is because documents are still in the Form of paper that is easily lost, damaged or lost, the information generated cannot be presented on time because it requires a long time to process data.  The Research Framework that will be carried out in solving the problem discussed is identifying, searching for information based on theoretical foundations, collecting data using observation and interview methods, analyzing to find solutions to the problems faced by Krisalia Wahyu Sari's Notary Office in Jambi City. The method uses the waterfall model, the implementation of this study uses the PHP Language and MySQL DBMS. To produce data processing applications that are expected to facilitate data processing and report generation.
Evaluasi Kinerja Machine Learning pada Klasifikasi Penyakit Jantung Menggunakan Teknik Penyeimbangan Data Eni Rohaini; Gunardi, Gunardi; Nurhayati Nurhayati; Jasmir Jasmir; Zahra Prisdian Tiararosa
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.59

Abstract

AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.
Implementasi YOLOv8 dan Pengaruh Augmentasi Data dalam Sistem Deteksi Faktor Risiko Sudden Infant Death Syndrom (SIDS) pada Bayi Rhadis Steffani Saputri; Jasmir Jasmir; Gunardi Gunardi
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.69

Abstract

Sudden Infant Death Syndrome (SIDS) is a sudden and unexpected death in infants that is often associated with the prone sleeping position. This study aims to develop an automated monitoring system capable of detecting SIDS risk factors using the YOLOv8 algorithm and to analyze the effect of data augmentation on model performance. The dataset consists of two classes, baby-lying-on-back (supine) and baby-lying-on-stomach (prone), which were processed through model training and evaluation using precision, recall, F1-score, and mAP metrics. The model was trained under two scenarios, without data augmentation and with data augmentation. The results show that the model without augmentation achieved a precision of 90%, recall of 85%, F1-score of 86%, and mAP50 of 93.7%. After applying augmentation, performance improved to a precision of 90%, recall of 87%, F1-score of 88%, and mAP50 of 95.1%. These findings indicate that augmentation increases detection accuracy and enhances model generalization, including robustness against variations in lighting and camera angles. Furthermore, testing with image and video inputs revealed that the non-augmented model exhibited a tendency toward overfitting, particularly in favor of the baby-lying-on-stomach, whereas the augmented model successfully classified both classes accurately. The developed system is also equipped with an alarm feature and early-warning notifications via Telegram to smartphone when a prone position is detected for a certain duration. Overall, the results demonstrate that YOLOv8 with data augmentation is effective for an automated, non-invasive monitoring system for infants, making it suitable for detecting and preventing potential SIDS risk factors.
Klasifikasi Sentimen Ulasan Produk Olahraga di Tokopedia Menggunakan Metode Machine Learning dengan Pendekatan TF-IDF Fransiskus Dapot Sihaloho; Jasmir Jasmir; Gunardi Gunardi
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.130

Abstract

The rapid growth of e-commerce platforms in Indonesia, particularly Tokopedia, has resulted in a large volume of consumer reviews containing valuable information regarding customer perceptions and satisfaction. However, manual analysis of such reviews is inefficient and prone to subjectivity, necessitating an automated approach based on machine learning. This study aims to classify the sentiment of sports product reviews on Tokopedia into positive, negative, and neutral categories by applying Logistic Regression, Support Vector Machine (SVM), and Random Forest using the Term Frequency–Inverse Document Frequency (TF-IDF) approach. The data were collected through web scraping of Indonesian-language sports product reviews and processed through several preprocessing stages, including data cleaning, case folding, tokenization, stopword removal, and stemming. Feature representation was performed using TF-IDF to transform textual data into numerical vectors, after which the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the application of TF-IDF significantly improves the performance of all models, with SVM consistently achieving the most optimal performance compared to Logistic Regression and Random Forest. These findings demonstrate that classical machine learning algorithms combined with TF-IDF remain highly effective for sentiment analysis of Indonesian-language text. The implications of this study are expected to assist sellers in understanding customer opinions, support consumers in making informed purchasing decisions, and serve as a foundation for the development of sentiment analysis and recommendation systems on e-commerce platforms.
An Adaptive Feature-Aware Hybrid Resampling Strategy for Imbalanced Diabetes Classification with Integrated Balanced Index Evaluation Jasmir Jasmir; Riza Pahlevi; Gunardi Gunardi; Eni Rohaini; Tiko Nur Annisa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7418

Abstract

Class imbalance remains a critical challenge in medical data classification, particularly in diabetes prediction, as it significantly degrades minority-class sensitivity. This study proposes an Adaptive Feature-Aware Hybrid Resampling Strategy (AHRS) that dynamically integrates oversampling and undersampling based on Imbalance Ratio (IR) and Feature Importance (FI). Unlike conventional static resampling methods, AHRS iteratively adjusts class distribution while preserving informative feature structures. In addition, this study introduces the Integrated Balanced Index (IBI), a bounded composite metric integrating precision, recall, and specificity to provide a fairer evaluation of classification performance on imbalanced medical datasets. The proposed approach was evaluated using the Pima Indian Diabetes Dataset (768 instances) with K-Nearest Neighbor, Naïve Bayes, and Random Forest classifiers under 5-fold stratified cross-validation. Experimental results demonstrate that AHRS consistently outperforms SMOTE, Random Oversampling, and Tomek Links, achieving accuracy improvements of 5–7% and recall gains of up to 10%. Random Forest combined with AHRS achieved the highest IBI score of 0.90, indicating strong balance between sensitivity and specificity. The findings suggest that adaptive, feature-aware resampling combined with balanced evaluation metrics provides a reliable and interpretable framework for fair medical classification systems and Clinical Decision Support Systems (CDSS).
Co-Authors Abdussalaam, Falaah Ahmad Husaein Arvita, Yulia Asyhadi, Ahmad Ayu Feranika Bambang Sukoco Bayu Perdana Calista, Stephanie Dwi Sukma Ramdhani Eddy Suratno Effiyaldi, Effiyaldi Fadilatul Muhamad Feranika, Ayu Fransiskus Dapot Sihaloho Ghea Permata Rizky Gunardi Husaein, Ahmad Jasmir , Jasmir Jasmir Jasmir Jasmir Jasmir Jasmir, Jasmir Jelita Damai Sofia Lorenza Sihite Johni Paul Karolus Pasaribu Joni Devitra Kasrizal Kasrizal Kristeria, Kris Louis Sebastian Anot Putra MARDIANA Maria Rosario Borroek Melani Kadar Miranda Miranda Muhammad Riza Pahlevi NAIBAHO, RONALD Novita, Dwi Nurhayati Nurhayati Pareza Alam Jusia, Pareza Alam Pratomo, Dwi Joko Purnama, Benni Rajhu Ilham Pradana Rhadis Steffani Saputri Rohaini, Eni Salsa Della Anggraini Selfi Kumara Hati Sharipuddin, Sharipuddin Sherin Devanty Situmorang Stanley Muljadi Art Sukipardika, Abel Suryadillah Hendrawinata Tiko Nur Annisa Vira Aryati Aryati William Zaenal Abidin, Dodo Zahra Prisdian Tiararosa