Article Per Year (5 Year)
p-Index From 2021 - 2026
4.129
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Khatulistiwa Informatika SMARTICS Journal CYBERNETICS BULETIN AL-RIBAATH Jurnal Tekno Insentif PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Teknika Teknosains : Jurnal Sains,Teknologi dan Informatika Innovation in Research of Informatics (INNOVATICS) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) INSERT: Information System and Emerging Technology Journal JUTECH : Journal Education and Technology JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) SmartComp KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Rachmat Wahid Saleh Insani
Universitas Muhammadiyah Pontianak
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 33 Documents
Search

 1   2   3   4 
Klasifikasi Status Gizi Balita Menggunakan Algoritma Random Forest Sabima, Muhamad Angga Rizki; Octariadi, Barry Ceasar; Insani, Rachmat Wahid Saleh
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2415

Abstract

Nutritional problems in children under five remain a major challenge in Indonesia's health development as they can affect growth, cognitive abilities, and future productivity. The Indonesian Nutritional Status Survey (SSGI) serves as an important data source to understand the nutritional condition of children under five. This study aims to classify the nutritional status of children under five using the Random Forest algorithm with a Knowledge Discovery in Database (KDD) approach. The research stages include data cleaning, preprocessing, feature selection, modeling, and model performance evaluation. The data were obtained from Puskesmas Gang Sehat.The results indicate that Random Forest can classify the nutritional status of children under five with high accuracy for the majority class (Normal Nutrition), while performance for the minority class (Abnormal Nutrition) can still be improved. This demonstrates that the Random Forest algorithm is effective for classifying nutritional status, although optimizing data imbalance and adding supporting variables can enhance results for the minority class. This study is expected to contribute to the development of technology-based solutions for addressing nutritional issues in children under five.
Comparison Analysis of Equivalence Class Partitioning and Boundary Value Analysis Techniques in Software Quality Testing of ReservasiPolnep Application Alifiansyah, Zuhrie; Alkadri, Syarifah Putri Agustini; Insani, Rachmat Wahid Saleh
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16789

Abstract

Software testing is a crucial phase before the official launch of an application to ensure its functionality and quality. This study compares two black box testing techniques—Equivalence Class Partitioning (ECP) and Boundary Value Analysis (BVA)—in identifying functional defects in the ReservasiPolnep application. The study involved testing key application features using both techniques, and results were measured using standard software testing metrics: test case coverage, success rates, test time, and cost per defect. The results showed that ECP is more time and cost-efficient, requiring only 26 test cases and 15 minutes 27 seconds per test, with a cost of Rp30 per defect and an 84.6% success rate. In contrast, BVA covers more test scenarios with 36 test cases, taking 27 minutes 5 seconds and costing Rp40 per defect, with a slightly higher success rate of 86.1%. The study concludes that each technique has advantages depending on the context, and highlights the need for input validation improvements in the application.
Penerapan Data Mining untuk Klasifikasi Tingkat Kepuasan Pelanggan Café Menggunakan Metode Decision Tree C4.5 Ariq, Atta Tha; Sucipto, Sucipto; Insani, Rachmat Wahid Saleh
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 15, No 1 (2026): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v15i1.9653

Abstract

Kepuasan pelanggan adalah faktor utama dalam meningkatkan reputasi bisnis, loyalitas pelanggan, dan efisiensi operasional. Penelitian ini bertujuan mengembangkan sistem yang memberikan informasi akurat tentang tingkat kepuasan dan ketidakpuasan pelanggan di sebuah café. Harapannya, temuan dari penelitian ini dapat memberikan dampak baik kepada café guna untuk meningkatkan kualitas pelayanan dengan mengetahui apa saja indikatot-indikator yang mempengaruhi tingkat kepuasan pelanggan café. Metode yang digunakan adalah Decision Tree C4.5, yang membangun pohon keputusan untuk klasifikasi. Proses meliputi penanganan missing value, pengecekan duplicate data, label encoding, penanganan data imbalance dengan SMOTE, pemodelan Decision Tree C4.5, pengecekan akurasi, dan visualisasi aturan keputusan. Evaluasi model dilakukan menggunakan metrik confusion matrix. Hasil evaluasi menunjukkan bahwa model klasifikasi memiliki performa sangat baik, dengan accuracy 98% pada data latih dan 93% pada data uji. Nilai recall, precision, dan F1-score masing-masing adalah 94%, 97%, dan 95%.
Co-Authors Alamsyah, Dedi Alda Cendekia Siregar Alifiansyah, Zuhrie Alkadri, Syarifah Putri Agustini Angga Prima Syahputra Anggreini, Sella Arfiyan, Fandika Roinaldi Ariq, Atta Tha Asrul Abdullah Barry Ceasar Octariadi Barry Ceasar Octariadi Chatarina Umbul Wahyuni Dedy Trianto Eko Julianto Elsa Damayanti Enkan Feny Nopitasari Gusti, Gusti Muhamad Adzaky Masroni MHD. Reza M.I. Pulungan Muhammad Agus Muljanto Muhammad Hendri Nasim, Muhammad Octariadi, Barry Ceasar PRAYOGI, ADITYA PUTU SUGIARTAWAN Reyhan Roni, Roni Rustina, I Dewa Ketut Rai Sabima, Muhamad Angga Rizki Siregar, Alda Cendekia Siregar, Alda Cendekia Sucipto Sucipto Sucipto Sucipto Sumirah Sumirah Sumirah Sumirah Syarifah Putri Agustini Alkadri Syarifah Putri Agustini, Syarifah Putri Vika Ummu Hani Wahyu Fajar Wicaksono Wahyu Nugroho Ya' Ilham Eka Saputra