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Insearch: Information System Research Journal
Published by Universitas Islam Negeri Imam Bonjol Padang
ISSN : -     EISSN : 27754669     DOI : 10.15548
Core Subject : Science, Education, Social, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences Other
Insearch focus on the dissemination of scientific research in the field of Information System and Information Technology. The journals scope includes but not limited to the following fields IS Development and Management, IS Project Management, IS Quality and Satisfaction Analysis, Enterprise Resource Planning System, Customer Relationship Management System, Supply Chain Management System, IT Audit and its Methodology, IT Governance and its Methodology, Human Computer Interaction, Social Informatics, Information Security and Risk Management, Cryptography and its application, Distance Learning and its application, Geographic Information System, Decision Support System, Expert System, Data Mining, Big Data and its application.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 56 Documents
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Penerapan Algoritma CatBoost untuk Estimasi Rencana Anggaran Biaya (RAB) Proyek Berbasis Bill of Quantities (BOQ) Galbi Nadifah
Insearch: Information System Research Journal Vol 6, No 01 (2026): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v6i01.13609

Abstract

Estimasi Rencana Anggaran Biaya (RAB) yang akurat merupakan elemen krusial dalam keberhasilan proyek konstruksi infrastruktur telekomunikasi. Metode dalam menentukan harga satuan pada dokumen Bill of Quantities (BOQ) seringkali tidak efisien dan rentan terhadap penaksir subjektivitas. Tantangan utama dalam otomatisasi estimasi ini adalah karakteristik data BOQ yang didominasi oleh fitur kategorikal (teks) dengan kardinalitas tinggi. Penelitian ini mengusulkan penerapan algoritma Machine Learning CatBoost (Categorical Boosting) untuk memprediksi harga satuan pekerjaan. Penelitian mengikuti alur sistematis mulai dari pengumpulan data historis, pra-pemrosesan data menggunakan transformasi logaritma, hingga pelatihan model. Hasil eksperimen menunjukkan bahwa CatBoost mampu menangani fitur kategorikal secara efektif. Evaluasi model menghasilkan nilai Coefisien Determinasi ( R 2 ) sebesar 97,66% dan Mean Absolute Error ( MAE ) sebesar Rp 13.289, yang lebih unggul dibandingkan algoritma pembanding XGBoost ( R 2 96,82%). Manual validasi menunjukkan rasio kesalahan prediksi hanya 0,05 dari total nilai proyek, menunjukkan bahwa model ini layak diterapkan untuk meningkatkan efisiensi dan akurasi komputasi RAB.
Business Process Reengineering Sistem Administrasi Pada Kelurahan Sukodadi Muhammad Aqbil Faza Rachman; Wildan Suharso
Insearch: Information System Research Journal Vol 6, No 01 (2026): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v6i01.13251

Abstract

Penelitian ini bertujuan untuk menganalisis, merancang, dan mengusulkan model optimalisasi alur kerja dokumen administrasi di Kelurahan Sukodadi melalui pendekatan Business Process Reengineering (BPR) guna mengatasi permasalahan mendasar seperti waktu pemrosesan yang lama, redundansi pekerjaan, dan rendahnya transparansi layanan. Era transformasi digital menuntut adaptasi sektor pemerintahan sebagai garda terdepan pelayanan publik, menjadikan perbaikan proses kerja fundamental sebagai kebutuhan mendesak untuk peningkatan kinerja pegawai. Metode BPR diterapkan secara fundamental dan radikal untuk merancang ulang proses bisnis, dengan fokus pada pemanfaatan teknologi informasi (TI) melalui prototipe sistem berbasis website. Hasil perbandingan efisiensi throughput menunjukkan bahwa rancangan baru berbasis digital secara dramatis menurunkan waktu layanan, mengeliminasi aktivitas non-nilai tambah, dan mengurangi total aktivitas operasional dari 13 menjadi 7, yang secara langsung menekan biaya operasional dan potensi human error. Penerapan solusi ini diharapkan mampu meningkatkan efektivitas, efisiensi, dan akuntabilitas pelayanan administrasi serta memberikan dampak positif yang terukur terhadap peningkatan kinerja pegawai di Kelurahan Sukodadi.
Teknologi Digital Berbasis Pembelajaran Mesin Sederhana untuk Mendukung Pengambilan Keputusan Produksi Makanan yang Aman dan Efisien dalam Praktik Boga Ifnalia Rahayu; wiki lofandri; Fauza Afni
Insearch: Information System Research Journal Vol 6, No 02 (2026): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v6i02.13735

Abstract

Perkembangan teknologi digital telah mendorong transformasi dalam praktik produksi makanan, termasuk pada bidang boga. Tantangan utama dalam praktik boga adalah menjaga konsistensi kualitas, efisiensi proses, dan keamanan pangan, terutama pada skala pendidikan vokasi dan unit produksi kecil. Penelitian ini bertujuan mengkaji pemanfaatan teknologi digital yang dipadukan dengan pembelajaran mesin ringan sebagai alat bantu pengambilan keputusan produksi makanan. Metode pembelajaran mesin yang digunakan adalah decision tree karena sifatnya yang sederhana dan mudah diinterpretasikan. Data dikumpulkan dari proses produksi makanan yang mencakup parameter waktu pemasakan, suhu, dan hasil kualitas produk. Hasil penelitian menunjukkan bahwa pemanfaatan teknologi digital yang didukung pembelajaran mesin ringan mampu membantu praktisi boga dalam mengidentifikasi pola proses produksi serta memberikan rekomendasi keputusan yang lebih sistematis. Penelitian ini menegaskan bahwa integrasi teknologi digital dan pembelajaran mesin ringan berpotensi meningkatkan keamanan dan efisiensi produksi makanan tanpa menghilangkan peran keahlian manusia
Integrasi Deep Neural Network dan Rule-Based Reasoning dalam Sistem Pakar untuk Diagnosis Gangguan Sistem Saraf Yaslinda Lin Lizar
Insearch: Information System Research Journal Vol 6, No 02 (2026): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v6i02.13728

Abstract

The advancement of artificial intelligence in healthcare has encouraged the use of deep learning to support medical diagnosis. However, Deep Neural Network (DNN) models suffer from low interpretability due to their black-box nature, which limits clinical applicability. This study aims to integrate DNN and Rule-Based Reasoning (RBR) into an expert system to provide accurate and explainable neurological disorder diagnosis. The dataset consists of 400 clinical patient records covering four diagnostic classes: peripheral neuropathy, transient ischemic attack (TIA), acute migraine, and epilepsy. The DNN model employs a multilayer perceptron architecture with two hidden layers and ReLU activation, while RBR applies IF–THEN rules derived from expert knowledge. The integration mechanism combines DNN probability and rule-based certainty factors through weighted scoring. Experimental results show an accuracy of 91%, precision of 0.90, recall of 0.89, and F1-score of 0.895. Expert validation indicates an 86% confidence level, demonstrating that the proposed system is suitable as an explainable artificial intelligence-based diagnostic support tool.
Evaluasi Kinerja Sistem Informasi Peluang Investasi Berbasis GIS di Kabupaten Lampung Selatan Menggunakan Metode Black Box Testing Ridho Sholehurrohman; Rio Gismara; Firdaus Firdaus; Handoyo Widi Nugroho
Insearch: Information System Research Journal Vol 6, No 02 (2026): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v6i02.13778

Abstract

The utilization of Geographic Information Systems (GIS) in mapping investment opportunities has become a strategic solution to support location-based decision-making in South Lampung Regency. However, the effectiveness of such systems needs to be evaluated to ensure that all functionalities operate according to user requirements. This study aims to evaluate the performance of a GIS-based Investment Opportunity Information System using the Black Box Testing method. The testing process involved developers, administrators, and eight users from regional government organizations (OPD) to evaluate various system modules, including registration, login, GIS mapping, data visualization, and reporting. The results indicate that all system functions operate as expected and produce outputs consistent with the given inputs. Nevertheless, minor issues were identified in the graphical visualization display on certain devices. This study contributes to ensuring the functional quality of the system and provides recommendations for further system improvement
The Impact of Signal Deletion in Text Preprocessing on Timestamp Comment Detection under Severe Class Imbalance decha danillo novicahyanto; Albertus Dwivoga Widiantoro
Insearch: Information System Research Journal Vol 6, No 02 (2026): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v6i02.14780

Abstract

Timestamping in YouTube comment sections is a distinctive form of participatory content curation. This study examines why four text classification algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, and XGBoost, fail completely at detecting timestamp-bearing comments in an informal Indonesian-language corpus. Across 14,149 comments from Ruangguru Clash of Champions videos, the imbalance ratio reaches 73.86:1. Every model returns an accuracy near 98.7% with a minority-class recall of zero, a Balanced Accuracy of 0.50, and Cohen’s Kappa at or below zero, the textbook signature of the Accuracy Paradox. The contribution is diagnostic: class imbalance is not the sole cause. A token-level trace shows that punctuation removal followed by standalone-numeral removal deterministically destroys the pattern defining the positive label, while sparse-term pruning at a 0.99 threshold discards vocabulary occurring in fewer than 1% of documents, above the minority prevalence of 1.34%. Recovering the confusion matrices permits a complete imbalance-robust metric set to be computed, confirming that all four models are indistinguishable from a constant classifier that ignores its input. The study specifies the ablation required to separate these causes, and establishes that before minority-class failure is attributed to imbalance, researchers must verify that preprocessing has not deleted the signal being learned.

Page 6 of 6 | Total Record : 56

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