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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pilar Nusa Mandiri INTI Nusa Mandiri Jurnal Informatika dan Rekayasa Elektronik Jurnal Teknik Informatika (JUTIF) Jurnal Sistem Informasi SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Teknologi Sistem Informasi Paradigma Jurnal Aruna Mengabdi (ARMI)
Ganda Wijaya, Ganda
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Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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OPTIMALISASI POHON KEPUTUSAN ID3 MENGGUNAKAN PARTICLE SWARM OPTIMIZATION DALAM PREDIKSI ADOPSI LAYANAN DIGITAL PAYMENT Sumarna; Wijaya, Ganda; Suryadithia, Rachmat; Pangesti, Witriana Endah; Yudhistira
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 2 (2025): JIRE November 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v8i2.1711

Abstract

Transformasi digital mendorong peningkatan penggunaan layanan pembayaran digital seperti OVO dan GoPay. Namun, tingkat penggunaan layanan ini belum merata, sehingga diperlukan model prediksi untuk memahami faktor-faktor yang memengaruhi keputusan pengguna dalam mengadopsi layanan tersebut. Penelitian ini mengembangkan model klasifikasi berbasis algoritma ID3 yang dioptimasi menggunakan Particle Swarm Optimization (PSO). Data dikumpulkan melalui kuesioner dari 750 responden, kemudian diproses melalui tahap preprocessing, pelatihan ID3, dan optimasi dengan PSO. Hasil menunjukkan bahwa model ID3+PSO mencapai akurasi 94,53%, lebih tinggi dibandingkan ID3 tanpa optimasi (92,93%). Precision dan recall masing-masing meningkat menjadi 95,41% dan 95,15%, sementara AUC tetap tinggi di angka 98,20%. PSO terbukti efektif menyederhanakan model dan meningkatkan performa klasifikasi. Temuan ini berimplikasi pada peningkatan akurasi sistem rekomendasi dan pengambilan keputusan strategis oleh penyedia layanan digital payment, terutama dalam memahami karakteristik serta potensi adopsi layanan oleh pengguna secara lebih tepat.
A Statistical Benchmarking of Imbalance-Aware Ensemble Models for Cervical Cancer Prediction Sumarna, Sumarna; Astrilyana, Astrilyana; Sugiono, Sugiono; Wijaya, Ganda; Desvia, Yessica Fara
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15995

Abstract

Cervical cancer remains one of the leading causes of cancer-related mortality among women worldwide, particularly in developing countries. Early prediction through machine learning has the potential to support clinical decision-making; however, cervical cancer datasets often suffer from severe class imbalance, which reduces the ability of conventional models to correctly detect minority cases. This study aims to improve minority class detection in cervical cancer prediction by evaluating several imbalance-aware ensemble learning approaches. The proposed study compares five models, namely Random Forest (RF), SMOTE combined with Random Forest (SMOTE+RF), Balanced Random Forest (BRF), EasyEnsemble, and RUSBoost. The models were evaluated using 5-fold cross-validation with performance metrics including accuracy, recall, F1-score, and Area Under the Curve (AUC). Statistical validation was conducted using the Friedman test, followed by the Wilcoxon signed-rank test and Kendall’s W effect size analysis to assess the significance and magnitude of performance differences. Unlike prior studies that primarily focus on performance improvement, this study introduces a statistically rigorous comparative evaluation to assess both significance and practical effect of imbalance-aware ensemble methods. Experimental results show that imbalance-aware ensemble methods significantly improve minority detection compared to the baseline RF model. In particular, BRF achieved the highest AUC of 0.9469 with improved recall stability, while RUSBoost produced the highest F1-score of 0.7451. Although the Friedman test indicated no statistically significant difference among models (p = 0.2037), the Kendall’s W value of 0.297 suggests a small-to-moderate practical effect. These findings indicate that imbalance-aware ensemble learning can enhance the robustness of cervical cancer prediction models, particularly for minority class detection. The results highlight the importance of incorporating imbalance-handling strategies in medical prediction systems and suggest potential directions for future research in improving diagnostic decision-support models.
Pengukuran Kualitas Website Skill Academy Terhadap Kepuasan Pengguna Menggunakan Metode Webqual 4.0 Habiba, Izmi; Wijaya, Ganda
Paradigma - Jurnal Komputer dan Informatika Vol. 24 No. 1 (2022): Periode Maret 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/paradigma.v24i1.963

Abstract

Development service or self-training currently has transformed along with the development of digital technology. Skill Academy becomes a solution in the community to develop technical and soft skills without constraints such as the limitation of time, place, and energy. However, the Skill Academy website sometimes still experiences errors, such as material or tutorial videos cannot be played and certificates not being printed according to the schedule. Therefore, this study aims to find out how the quality of the Skill Academy website on the user satisfaction using the Webqual 4.0 method. Webqual 4.0 method has three instruments, Usability, Information Quality, and Service Interaction Quality. The primary data was obtained by disseminating a questionnaire containing 22 questions about website quality and 5 questions about user satisfaction. The data were obtained using a non-probability sampling technique, which was purposive, with 100 respondents. The data analysis method used was multiple linear regression using SPSS 25 tools. The results of the study showed that the three Webqual 4.0 instruments simultaneously had an impact on user satisfaction or 58.1%, while the rest was defined by other factors outside this research model. The results for the influence of each instrument were found that Usability did not have significant influence, while Information Quality and Service Interaction Quality had a significant influence on User Satisfaction.
Optimization of the YOLOv7 Object Detection Algorithm for Estimating the Amount of Apple Harvest Riyanto, Verry; Nawawi, Imam; Ridwansyah, Ridwansyah; Wijaya, Ganda; Haryanto, Toto
Paradigma - Jurnal Komputer dan Informatika Vol. 25 No. 1 (2023): March 2023 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v25i1.1809

Abstract

The increasing population consumed in high production and food needs for survival. Apples are one of the crop harvest products in Indonesia whose needs are increasing, because they are not only needed for human vitamins but can be used as hand fruit or a form of gratitude to those who receive the fruit. In the process of harvesting apples in agricultural land, harvesting is often found which is not feasible in the hands of consumers because it takes too long for apples to not be harvested when the condition of the fruit is feasible in maturity. Therefore, the authors approach this problem by processing the image results obtained to form a detection model, whether the apples are said to be feasible to be harvested immediately and from the image results it can also be calculated the number of fruits captured by the image model , feature enhancements Estimates on objects from this image model are expected to provide more timely harvest predictions in order to provide longer aging of apples and good fruit quality after reaching consumers
Co-Authors Achmad Maezar Bayu Aji Adjat Sudradjat Agung Baitul Hikmah Ali Mustopa, Ali Arief Rusman Astrilyana Astrilyana, Astrilyana Desvia, Yessica Fara Habiba, Izmi Henny Destiana Herlina, Maria Imam Nawawi, Imam Jajang Jaya Purnama Kahfi, Ahmad Hafidzul Kholifah, Desiana Nur Mahmud Nawawi, Hendri Mochamad Wahyudi Olivia, Shinta Pratama, Febryawan Yuda Rachman Komarudin Ridwansyah Ridwansyah Ristiyani, Lina Ariska Rudianto, Biktra Siswahyudianto Sugiono, Sugiono Suhardjono S Sumanto Sumanto Sumarna Sumarna Sumarna Suryadithia, Rachmat Toto Haryanto Tussyifa, Zaqia Verry Riyanto Wawan Setiawan WITRIANA ENDAH PANGESTI Yudhistira Yudhistira Yudhistira