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All Journal Journal of Software Engineering JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research Jurnal Mantik INTI Nusa Mandiri Computer Science (CO-SCIENCE) INSANtek – Jurnal Inovasi dan Sains Teknik Elektro Journal Automation Computer Information System (JACIS) Malcom: Indonesian Journal of Machine Learning and Computer Science
Harsih Rianto
Universitas Bina Sarana Informatika
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Neuroscience Transportation Other

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SOFTWARE DEFECT PREDICTION TRENDS: A BIBLIOMETRIC ANALYSIS OF MACHINE AND DEEP LEARNING Harsih Rianto; Omar Pahlevi; Desmulyati; Amrin; Ade Surya Budiman; Budi Supriyadi
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.v11i3.7351

Abstract

This study provides a comprehensive bibliometric mapping of global research trends and emerging frontiers in Software Defect Prediction (SDP), emphasizing the integration of machine learning (ML) and deep learning (DL) approaches. Unlike previous bibliometric surveys that focused narrowly on metric-based or short-term analyses, this work offers a broader and more integrated perspective on the intellectual evolution, collaboration patterns, and thematic directions in SDP research. Using data retrieved from the Scopus database and analyzed through Bibliometrix and VOSviewer, the study systematically applied the PRISMA protocol to ensure transparency and replicability. A total of 1,549 publications were examined, revealing a steady increase in scientific output dominated by China, India, and the United States. Thematic and keyword analyses identified five core clusters that trace the paradigm shift from traditional statistical models to advanced ML- and DL-driven predictive frameworks. Emerging topics such as transfer learning, cross-project prediction, and explainable AI (XAI) were identified as promising frontiers shaping the next phase of software quality prediction research. Beyond mapping academic progress, this study contributes strategic insights for researchers seeking to identify research gaps, industry practitioners developing intelligent defect prediction tools, and policymakers designing AI-driven software quality initiatives
Rancang Bangun Sistem Informasi Inventory Menggunakan Metode Rapid Application Development Harsih Rianto; Amrin Amrin
INSANtek Vol. 4 No. 1 (2023): Mei 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/instk.v4i1.1942

Abstract

Perusahaan retail di Indonesia memerlukan sistem informasi inventory yang efektif untuk memberikan pelayanan yang lebih baik kepada pelanggan dan meningkatkan efisiensi pengelolaan inventory. Saat ini, sistem inventory yang ada masih sederhana dan manual, sehingga rentan mengalami kesalahan dalam pengolahan data dan masalah penyimpanan dokumen. Dengan menerapkan sistem informasi inventory yang terkomputerisasi menggunakan perangkat lunak yang tepat, perusahaan dapat mengatasi masalah tersebut dan meningkatkan akurasi serta efisiensi pengelolaan inventory. Hal ini juga dapat meningkatkan kepuasan pelanggan dan membantu perusahaan menjadi lebih kompetitif di pasar. Untuk mengatasi masalah dalam sistem inventory yang sederhana dan manual, perusahaan retail di Indonesia perlu menerapkan sistem informasi inventory yang terkomputerisasi dengan menggunakan metode Rapid Application Development (RAD). Metode ini dimulai dari perencanaan, perancangan, dan implementasi dengan tujuan un tuk mengembangkan sistem yang cepat, fleksibel, dan efektif. Dengan menerapkan sistem informasi inventory dengan metode RAD, perusahaan dapat mengatasi masalah seperti kesalahan dalam pengolahan data dan masalah penyimpanan dokumen serta meningkatkan efisiensi dan akurasi pengelolaan inventory. Hal ini juga dapat membantu perusahaan meningkatkan kepuasan pelanggan dan bersaing lebih efektif di pasar.   Retail companies in Indonesia require an effective inventory information system to provide better service to customers and increase efficiency in inventory management. Currently, the existing inventory system is still simple and manual, making it prone to errors in data processing and document storage issues. By implementing a computerized inventory information system using suitable software, companies can address these issues and improve the accuracy and efficiency of inventory management. This can also enhance customer satisfaction and help companies become more competitive in the market. To address the issues with the simple and manual inventory system, retail companies in Indonesia need to implement a computerized inventory information system using the Rapid Application Development (RAD) method. This method involves planning, designing, and implementing a system that is fast, flexible, and effective. By applying the RAD method to the inventory information system, companies can overcome issues such as data processing errors and document storage issues, while improving the efficiency and accuracy of inventory management. This can also help companies enhance customer satisfaction and compete more effectively in the market.
Perancangan Sistem Absensi Siswa Berbasis Quick Response (QR) Code Menggunakan Framework JavaScript Rafli Naufal Alief; Harsih Rianto
INSANtek Vol. 6 No. 2 (2025): November 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/insantek.v6i2.10185

Abstract

Absensi merupakan komponen penting dalam sistem administrasi sekolah untuk menunjang kedisiplinan dan tanggung jawab siswa. Di SMP Islam Annasiriin, proses absensi masih dilakukan secara manual menggunakan buku absen, yang rawan kesalahan pencatatan, manipulasi data, serta keterlambatan dalam rekapitulasi. Oleh karena itu, dirancang sebuah sistem absensi berbasis Quick Response (QR) Code yang terintegrasi secara digital.Aplikasi ini dikembangkan menggunakan framework JavaScript, yaitu React.js pada sisi frontend dan Express.js pada sisi backend, serta MySQL sebagai sistem basis data. Penelitian ini menerapkan metode pengembangan perangkat lunak Waterfall, dengan tahapan observasi, wawancara, studi pustaka, perancangan sistem, implementasi, pengujian, dan pemeliharaan.Sistem memungkinkan guru mencatat kehadiran siswa secara otomatis melalui pemindaian QR Code, yang langsung disimpan ke dalam database dan dapat direkap oleh admin. Pengujian menggunakan metode Black Box menunjukkan semua fitur, seperti login, input data siswa, absensi QR, hingga laporan kehadiran, berjalan dengan baik.Hasil penelitian ini membuktikan bahwa sistem absensi berbasis QR Code meningkatkan efisiensi, akurasi, dan keamanan data. Sistem ini diharapkan menjadi solusi terhadap permasalahan absensi manual dan dapat dikembangkan menjadi sistem informasi akademik terpadu.
Improving Liver Disease Diagnosis Accuracy Using Synthetic Minority Oversampling Technique and Particle Swarm Optimization Harsih Rianto; Amrin amrin
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.17943

Abstract

This study aims to improve liver disease diagnosis accuracy using the Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO). The dataset used in this study is the Indian Liver Patient Dataset (ILPD), which consists of 583 patient records with 10 input attributes and one class attribute. The main problem in this dataset is the imbalance of class distribution between liver and non-liver patients, which may affect the performance of classification models. The research stages include data preprocessing, missing value imputation, stratified 70:30 train-test splitting, applying SMOTE to the training data, feature selection using PSO, and evaluating several classification models, namely Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and Logistic Regression (LR). The experimental results show that SMOTE-RF achieved the best overall performance with an AUC of 0.8610, accuracy of 0.7784, F1-score of 0.7783, and MCC of 0.5570. Meanwhile, SMOTE-PSO-DT improved the Tree model by selecting five important attributes, namely Direct Bilirubin, SGOT, Total Proteins, Albumin, and A/G Ratio. These results indicate that SMOTE effectively improves classification performance, while PSO helps simplify the model through feature selection.
INTEGRASI METODE SAMPLE BOOTSTRAPPING DAN WEIGHTED PRINCIPAL COMPONENT ANALISYS (PCA) UNTUK MENINGKATKAN PERFORMA NAÏVE BAYES PADA CITRA TUNGGAL PAP SMEAR Yumi Novita Dewi; Harsih Rianto; Dwiza Riana; Juarni Siregar
INTI Nusa Mandiri Vol. 14 No. 2 (2020): INTI Periode Februari 2020
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v14i2.1103

Abstract

Research on cervical cancer with the Pap Smear method is useful for finding pre-cancer diagnoses. Associated with previous research that the accuracy of the Naïve Bayes algorithm to the classification of a single Pap smear image still has an unsatisfactory accuracy. Whereas determining the class of single Pap cell smears is very important in determining whether these cells are normal or not. This study aims to determine whether integration using the Sample Bootstrapping (SB) method with the Weighted Principal Component Analysis (W-PCA) algorithm can improve the performance of the Naïve Bayes algorithm for seven different cell types. This model is the best solution used in the classification of datasets that are classified as having large dimensions. So that the integration of the two algorithms can increase the accuracy value to 87.24% for the seven classes and 97.30% for the two classes, and it can be concluded that with this integration model can improve the best accuracy value.
Co-Authors Adika May Sari Afiandri, Dhea Aulia AHMAD FAHRIZAL Alfian Alfian Amrin Amrin Amrin Amrin Astriana Mulyani Bimo Tri Cahyo Budi Supriyadi Budiman, Ade Surya Cahyani Budihartanti Citra Radita Utami Desmulyati Dwiza Riana Eka Wulansari Fridayanthie, Eka Wulansari Firstianty Wahyuhening Fibriany Hasan Basri Juarni Siregar khairul rizal Linda Marlinda Mohammad Syamsul Azis Noer Hikmah Nur Fadillah, Nur Omar Pahlevi Rafli Naufal Alief Rahmat Tirta Kusuma Rana, Dipo Yudhis Romi Satria Wahono Roris, Renaldi Putra Sahamony, Nur Fitriyani Saputra, Andhika Sari Dewi Siti Chaerul Fadilah Susi Susilowati, Susi Syamsiar, Syamsiar Syarifah, Asyifa Raudha Terttiaavini, Terttiaavini Tri Hartati Tyas Setiyorini Tyas Setiyorini Yamin Nuryamin Yesni Malau Yumi Novita Dewi