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INDONESIA
Journal of Computers and Digital Business
Published by PT Delitekno Media Mandiri
ISSN : -     EISSN : 28303121     DOI : 10.56427
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Journal of Computers and Digital Business is an interdisciplinary and open access journal covering Computers and Digital Business. The Journal of Computers and Digital Business is open to submission from experts and scholars in the wide areas of Information System, Security, Artificial Intelligent , Cloud Computing, Machine Learning, Digital Business Technology and other areas listed in the focus and scope of this journal. Focus and Scope Information System Information Security Information Retrieval Geographic Information System Fuzzy Logics Genetic Algorithms Neural Networks Machine Learning Decision Support System Data Mining Cloud Computing E-Learning E-Goverment E-Commerce E-Business Digital Business Management Digital Business Technology Digital Business Analysis & Design Big Data & Business Intelligence Cyber Security for Digital Business
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 96 Documents
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Penerapan Algoritma C4.5 dengan Reduced Error Pruning untuk Klasifikasi Tingkat Quarter Life Crisis Mahasiswa Elsa Valen Yunita Wijaya
Journal of Computers and Digital Business Vol. 5 No. 3 (2026): Articles in Press
Publisher : PT. Delitekno Media Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i3.1041

Abstract

Fase transisi dari dunia perkuliahan menuju dunia profesional seringkali memicu quarter life crisis pada mahasiswa tingkat akhir akibat tingginya tekanan akademik, sosial, dan ketidakpastian karir. Penelitian ini bertujuan membangun model klasifikasi tingkat quarter life crisis mahasiswa serta menganalisis efektivitas algoritma C4.5 yang dioptimalkan dengan teknik Reduced Error Pruning (REP) untuk mengatasi overfitting dan menyederhanakan struktur pohon keputusan. Data dikumpulkan dari 952 responden mahasiswa aktif melalui kuesioner yang mencakup variabel demografis dan psikologis, kemudian diproses melalui tahap persiapan data, pembentukan model dengan rasio pembagian data 70:15:15, dan evaluasi menggunakan confusion matrix. Atribut kesiapan karir, kejelasan karir, dan reaksi terhadap tekanan kerja teridentifikasi sebagai prediktor paling dominan berdasarkan nilai gain ratio tertinggi. Penerapan REP meningkatkan akurasi model dari 82,3% menjadi 87,4% dan menyederhanakan pohon dari 14 menjadi 10 simpul keputusan, sehingga aturan klasifikasi lebih mudah dibaca oleh praktisi konseling. Kebaruan penelitian ini terletak pada integrasi REP ke dalam algoritma C4.5 untuk data kuesioner psikologis mahasiswa Indonesia. Model yang dihasilkan terbukti efektif memberikan rekomendasi objektif bagi unit layanan konseling kampus dalam deteksi dini masalah psikologis mahasiswa.
Sistem Klasifikasi Lima Jenis Lesi Jerawat Berbasis Web dengan Transfer Learning MobileNetV2 Ramadhan, Syauqi Thoriq; Nuroji
Journal of Computers and Digital Business Vol. 5 No. 3 (2026): Articles in Press
Publisher : PT. Delitekno Media Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i3.1042

Abstract

Identifikasi jenis lesi jerawat secara visual sulit dilakukan karena beberapa kelas memiliki karakteristik yang serupa. Penelitian ini bertujuan mengembangkan sistem berbasis web untuk mengklasifikasikan lima jenis lesi jerawat menggunakan MobileNetV2 dengan transfer learning. Dataset Acne Dataset Image dari Kaggle berisi 2.778 citra yang mencakup blackheads, whiteheads, papules, pustules, dan cysts. Seluruh citra diubah ukurannya menjadi 224 × 224 piksel, sedangkan augmentasi hanya diterapkan pada data pelatihan. Model menggunakan bobot awal ImageNet dan fine-tuning pada 50 lapisan terakhir, kemudian dilatih selama 50 epoch dengan optimizer Adam, learning rate 0,00007, dan batch size 32. Kinerja model dievaluasi menggunakan akurasi, presisi, recall, F1-score, dan confusion matrix. Hasil pengujian menunjukkan bahwa model mencapai akurasi 99%, dengan presisi, recall, dan F1-score sebesar 0,98–1,00 pada lima kelas. Model selanjutnya diintegrasikan ke dalam aplikasi berbasis web untuk mengklasifikasikan citra yang diunggah pengguna dan menampilkan probabilitas setiap kelas. Hasil ini menunjukkan potensi MobileNetV2 sebagai pendukung klasifikasi awal lesi jerawat berbasis citra. Namun, pengujian pada data eksternal yang lebih beragam masih diperlukan untuk menilai kemampuan generalisasi model.
Extending UTAUT with Perceived Trust and Perceived Risk to Explain Telehealth Revisit Intention Among Young Adults in Jakarta, Indonesia: A PLS-SEM Study Ariadne Vihana Novalia; Frentzen Fernando Setiady; Yuli Eni
Journal of Computers and Digital Business Vol. 5 No. 3 (2026): Articles in Press
Publisher : PT. Delitekno Media Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i3.1049

Abstract

This study examines the determinants of young adults’ intention to revisit telehealth services in a post-pandemic developing-country setting. An extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework incorporating perceived trust and perceived risk was applied. Data were collected from 12 September to 31 October 2025 using non-probability purposive sampling, yielding 294 valid responses from young adults who had used private telehealth platforms in Jakarta, Indonesia. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS. The model explained 63.1% of the variance in behavioral intention. Perceived trust was the strongest predictor of revisit intention (β = 0.391, p < 0.001), followed by performance expectancy (β = 0.241, p < 0.001), social influence (β = 0.169, p = 0.006), and effort expectancy (β = 0.150, p = 0.019). Facilitating conditions and perceived risk were not significant. The findings extend the UTAUT framework to a post-adoption telehealth context in an urban developing-country market. Practically, sustaining revisit intention depends more on cultivating perceived trust through reliable service delivery, transparent data practices, and consistent platform performance than on technical support or direct risk mitigation.
Integrating Principal Component Analysis and Random Forest for Classifying University Students' Transportation Mode Choice in Medan, Indonesia Nurul Hanifa Lubis; Ismail Husein
Journal of Computers and Digital Business Vol. 5 No. 3 (2026): Articles in Press
Publisher : PT. Delitekno Media Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i3.1053

Abstract

Urban traffic congestion in Indonesian cities is closely linked to student commuting, yet the behavioral factors underlying mode choice are numerous and strongly correlated, which limits conventional classification models. This study integrates Principal Component Analysis (PCA) with a Random Forest (RF) classifier to predict transportation mode choice among university students in Medan, Indonesia. Data were obtained from 1,177 valid questionnaire responses covering 39 items, which were aggregated into eight behavioral constructs; seven were retained after sampling-adequacy screening. Applying the Kaiser criterion, two principal components were extracted, jointly explaining 70.42% of the total variance, and used as predictors of six transportation modes. The RF model attained 66.09% accuracy and a Cohen's Kappa of 0.5714, indicating moderate agreement between predicted and observed choices. Performance differed markedly across modes, from an F1-score of 82.37% for Mini Bus to 17.65% for Online Car Transportation, which was frequently misclassified as Online Motorcycle Transportation. The findings indicate that a compact two-component representation retains substantial behavioral information while yielding moderate predictive performance, and that mode-specific data enrichment is needed before such models can inform campus transportation planning.
Multi-Objective EV Routing with Recharging, Battery-Swapping, and K-Means Station Siting for Ride-Hailing Fleets in Medan City Widya Afriani; Fibri Rakhmawati
Journal of Computers and Digital Business Vol. 5 No. 3 (2026): Articles in Press
Publisher : PT. Delitekno Media Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i3.1054

Abstract

Electric vehicle adoption requires routing strategies that address travel efficiency and battery-energy constraints. This study develops a Multi-Objective Electric Vehicle Routing Problem (EVRP) model for two-wheeled electric ride-hailing services in Medan City, Indonesia. The model minimizes total travel distance and operational energy cost while incorporating recharging and battery-swapping strategies and identifying candidate sites for public charging stations (SPKLU) and battery-swapping stations (SPBKLU). It integrates route construction, battery-energy feasibility, recharge–swap assignment, multi-objective evaluation, and spatial clustering within one decision-support framework. Secondary data comprise one depot, 200 service points, four existing SPKLU, and nine existing SPBKLU. The Clarke–Wright Savings algorithm generates six routes, while the State of Charge (SOC) model evaluates battery feasibility. Twenty recharge–swap assignments are evaluated using the weighted sum method, and Pareto analysis identifies five non-dominated solutions. Under the cost-oriented weighting scenario, recharging is assigned to Routes 1, 4, and 6 and battery swapping to Routes 2, 3, and 5, yielding a travel distance of 327.79 km and an operational cost of IDR 39,131. This solution requires four SPKLU and three SPBKLU points, which K-Means clustering reduces to three and two candidate locations, respectively, providing guidance for battery-replenishment infrastructure planning.
Prioritizing Production Efficiency Factors in the Crumb Rubber Industry Using Fuzzy PROMETHEE: A Case Study at PT Pantja Surya Nursakila Ena Anjani; Rima Aprilia
Journal of Computers and Digital Business Vol. 5 No. 3 (2026): Articles in Press
Publisher : PT. Delitekno Media Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i3.1067

Abstract

Production efficiency directly affects a manufacturing company's productivity and competitiveness, yet prioritizing the factors that drive it is complicated by uncertainty and subjectivity in decision-makers' judgments. Fuzzy PROMETHEE has been applied to supplier selection, education, and renewable-energy decisions, but not to ranking production efficiency factors in the crumb rubber industry. This study addresses that gap by prioritizing nine production efficiency factors at PT Pantja Surya, a crumb rubber processor in Simalungun Regency, Indonesia. Fifty-three production employees rated four evaluation criteria and nine factors using linguistic variables, which were converted into Triangular Fuzzy Numbers and defuzzified by the Center of Area method. Pairwise preferences and Leaving, Entering, and Net Flow values were then computed following the PROMETHEE procedure. Raw material availability (A9, Net Flow = 0.938) ranked highest, followed by energy consumption (A6, 0.344), production downtime (A7, 0.250), and labor performance (A4, 0.125); production time efficiency (A1, -0.750) ranked lowest. The ranking offers management a structured basis for prioritizing raw material supply, energy use, and downtime reduction, although it rests on respondents' subjective judgments and should be treated as a decision aid rather than an objective measurement.

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