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All Journal Journal of Intelligent Decision Support System (IDSS) Jurnal Teknik Informatika (JUTIF) Dharma Nusantara: Jurnal Ilmiah Pemberdayaan dan Pengabdian kepada Masyarakat
Ichwani, Arief
Institut Teknologi Sumatera
Arts Humanities Computer Science & IT Education Social Sciences Other

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Web-based application development for the digitalization of badminton court reservation and scheduling using scrum methodology Ichwani, Arief; Idris, Mohamad; Afriansyah, Aidil
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 3 (2025): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i3.310

Abstract

The primary challenge in managing badminton court reservations and scheduling lies in the absence of an integrated system that supports booking, payment flexibility, and efficient schedule management. This study addresses these issues by developing a badminton court reservation information system equipped with user registration, login, court booking, schedule management, payment reporting, and member schedule search features. System evaluation was conducted through a User Acceptance Test (UAT), which confirmed that all core features functioned effectively and met user requirements. To further assess user experience, a Mean Opinion Score (MOS) evaluation involving five respondents and ten questions was carried out, yielding an average score of 3.8 on a four-point scale, categorized as very good. Respondents indicated that the system is easy to use, offers intuitive navigation, has an attractive interface, and provides stable performance while accelerating the booking process. These findings demonstrate that the system is both functionally reliable and well-received by users, thereby contributing to improved administrative efficiency, payment transparency, and overall user convenience.
Transformasi Media Pembelajaran Digital Melalui Aplikasi Edukatif Berbasis Android: Studi Kasus SMPN 1 Kalianda Meida Cahyo Untoro; Andika Setiawan; Arief Ichwani; Imam Eko Wicaksono; Mohamad Idris; Radhinka Bagaskara; Rahman Indra Kesuma; Raidah Hanifah; Leslie Anggraini
Dharma Nusantara: Jurnal Ilmiah Pemberdayaan dan Pengabdian kepada Masyarakat Vol. 4 No. 1 (2026): Dharma Nusantara: Jurnal Ilmiah Pemberdayaan dan Pengabdian kepada Masyarakat
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/dharma.v4i1.2317

Abstract

Disrupsi teknologi menuntut transformasi metode pembelajaran konvensional menuju ekosistem digital. Namun, tenaga pendidik di SMPN 1 Kalianda masih menghadapi kendala rendahnya kompetensi Technological Pedagogical Content Knowledge (TPACK) dalam mengembangkan media ajar mandiri, meskipun 80% siswa telah memiliki perangkat smartphone. Kegiatan pengabdian kepada masyarakat ini bertujuan menjembatani kesenjangan tersebut melalui pelatihan pembuatan aplikasi edukatif berbasis Android menggunakan platform no-code Kodular. Metode pelaksanaan terdiri dari tiga tahapan utama: persiapan awal, In-House Training (IHT) untuk asimilasi konsep UI/UX, dan lokakarya praktik langsung. Hasil kegiatan menunjukkan peningkatan kompetensi pedagogis-teknologis yang terukur secara signifikan. Pemahaman peserta melonjak dari ketiadaan literasi dasar (baseline 0%) menjadi capaian skor rata-rata evaluasi 90 dari 100 poin. Selain itu, 70% peserta terbukti berhasil mengintegrasikan Rencana Pelaksanaan Pembelajaran (RPP) ke dalam arsitektur prototipe aplikasi yang fungsional. Meskipun dihadapkan pada tantangan kelebihan kapasitas peserta dan rendahnya literasi komputasional dasar, pendekatan pendampingan adaptif terbukti efektif memitigasi hambatan tersebut. Sebagai luaran keberlanjutan program, inisiasi Komunitas Praktisi Guru Digital berhasil dibentuk guna memastikan adopsi media ajar inovatif secara berkesinambungan.
Preventing Data Leakage in Classification via Integrated Machine Learning Pipelines: Preprocessing, Feature Transformation, and Hyperparameter Tuning Ichwani, Arief; Kesuma, Rahman Indra; Setiawan, Andika; Wicaksono, Imam Eko; Hanifah, Raidah
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5490

Abstract

Data leakage in machine learning classification often leads to overfitting, inflated performance estimates, and poor reproducibility, which can undermine the reliability of deployed models and incur industrial losses. This paper addresses the leakage problem by proposing an integrated machine learning pipeline that strictly isolates training and evaluation processes across preprocessing, feature transformation, and model optimization stages. Experiments are conducted on the Titanic passenger survival dataset, where exploratory data analysis identifies data quality issues, followed by stratified train-test splitting to preserve class distribution. All preprocessing steps, including missing value imputation, categorical encoding, and feature scaling, are applied exclusively to the training data using a ColumnTransformer embedded within a unified Pipeline. A K-Nearest Neighbors (KNN) classifier is employed, with hyperparameters optimized via GridSearchCV and 3-fold cross-validation. Experimental results show that a baseline model without leakage control achieves only 72.62% test accuracy and exhibits a substantial overfitting gap. In contrast, the proposed pipeline-based approach improves generalization, achieving 78.21% test accuracy with an optimal configuration of k = 29 and Manhattan distance while significantly reducing overfitting. The main contribution of this work is the formulation of a reproducible, leakage-aware pipeline guideline that ensures unbiased evaluation and reliable generalization in classification tasks, providing practical methodological insights for both academic research and real-world machine learning applications.
Co-Authors Afriansyah, Aidil Andika Setiawan Andika Setiawan, Andika Idris, Mohamad Imam Eko Wicaksono Leslie Anggraini Meida Cahyo Untoro Mohamad Idris Radhinka Bagaskara Rahman Indra Kesuma Rahman Indra Kesuma Raidah Hanifah Raidah Hanifah Wicaksono, Imam Eko