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All Journal Teknika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Sistemasi: Jurnal Sistem Informasi INTECOMS: Journal of Information Technology and Computer Science Jurnal Sains dan Teknologi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Ilmiah Wahana Pendidikan International Journal for Applied Information Management AJAD : Jurnal Pengabdian kepada Masyarakat International Journal Software Engineering and Computer Science (IJSECS) Indonesian Journal of Multidisciplinary Science Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Artificial Intelligence and Digital Business Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal Innovations Computer Science
Setiawan, Kiki
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Religion Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Implementation of Educational Game System Using MDLC with Adobe Animate Application Setiawan, Kiki; Sarikah, Dede
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4727

Abstract

Digital gaming has transformed educational practices across Indonesia, creating new pathways for curriculum delivery through interactive entertainment. Educational games now serve as effective learning tools that capture student attention while building essential skills. The research develops and evaluates a multi-level educational puzzle game using Adobe Animate 2022, targeting cognitive skill enhancement and problem-solving abilities across different age groups. Development followed the Multimedia Development Life Cycle (MDLC) approach through six phases: conceptualization, design planning, resource gathering, system building, testing procedures, and final deployment. The puzzle application includes three difficulty levels with varying complexity parameters. Adobe Animate 2022 handled vector graphics creation, interactive programming, and multi-platform publishing. User evaluation involved testing across target demographics to assess usability and learning effectiveness. The finished application successfully demonstrates structured game development using MDLC principles. Testing showed positive user responses with balanced difficulty progression and completion rates that indicate effective entertainment-education integration. The development process provided organized workflows that supported quality control and user satisfaction goals. Adobe Animate 2022 proved capable for educational game creation, enabling smooth asset management and publishing operations. The study establishes a reproducible model for future educational gaming projects while validating game-based learning methods in Indonesian educational settings. Findings suggest that systematic development approaches produce superior educational outcomes compared to informal development practices.
Optimization of Classification Models for Customer Sentiment on Train Suite Class Compartments Using SMOTE and Particle Swarm Optimization Setiawan, Kiki; Miswanto, Miswanto; Zakaria, Aditya
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11617

Abstract

This study uses three algorithms, namely Naive Bayes (NB), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM). Then, the three methods are supplemented with the use of SMOTE (Synthetic Minority Oversampling Technique) and Particle Swarm Optimization (PSO), which will later be compared with the three methods to obtain good accuracy results. It is hoped that the use of SMOTE in this study can be a solution in handling imbalanced data, because the influence of imbalanced data is very large on the results of the model obtained, since algorithm processing that does not take into account data imbalance will tend to be dominated by the major class and ignore the minor class. Similarly, the use of Particle Swarm Optimization is expected to increase attribute weights and improve the accuracy of an algorithm and data classification. The model that obtained the best evaluation results was the Support Vector Model using SMOTE and Particle Swarm Optimization, with an accuracy value of 81.15%.
Implementation of Naïve Bayes for Public Sentiment Analysis on QRIS and GPN Digital Dominance through Instagram Nabilah, Laila; Setiawan, Kiki
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.337

Abstract

This study examines public sentiment toward the dominance of QRIS and GPN compared to Mastercard and Visa, using data collected from Instagram comments. Employing the Knowledge Discovery in Databases (KDD) methodology and the Naïve Bayes Classifier, the research analyzed 820 comments retrieved through automated scraping and processed using text mining techniques such as case folding, tokenization, stopword removal, stemming, and TF-IDF transformation. The model achieved an accuracy of 84.27%, a precision of 86.09%, a recall of 94.7%, and an F1-score of 90.21%, indicating strong reliability in identifying sentiment polarity. The analysis revealed that 76.5% of the comments expressed positive sentiment, highlighting users’ appreciation of QRIS and GPN for their convenience, speed, and accessibility across both micro and macro-scale transactions. Negative comments, representing 23.5%, centered on concerns about connectivity, data security, and trust in financial governance. These findings suggest that while QRIS and GPN have been widely embraced as efficient digital payment solutions, there remains a need for improved infrastructure, user education, and data protection. The study demonstrates the effectiveness of the Naïve Bayes algorithm for large-scale sentiment analysis in multilingual online environments and provides empirical insights for policymakers to strengthen Indonesia’s digital payment ecosystem.
Pengaruh Etos Kerja Islam Dan Upah Terhadap Loyalitas Pegawai Yayasan Pendidikan Pondok Pesantren Al Hasan Ciamis Setiawan, Kiki; Nila Nopianti; Mumtahaen, Ikmal
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.1912

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh etos kerja Islam dan upah terhadap loyalitas pegawai di Yayasan Pendidikan Pondok Pesantren Al Hasan Ciamis. Penelitian ini menggunakan pendekatan kuantitatif dengan metode deskriptif dan verifikatif. Sampel penelitian sebanyak 96 responden dipilih melalui teknik total sampling. Data diperoleh melalui kuesioner dan dianalisis menggunakan uji validitas, reliabilitas, asumsi klasik, regresi linier berganda, uji t, uji F, serta koefisien determinasi menggunakan bantuan software SPSS 25. Hasil penelitian menunjukkan bahwa secara parsial, etos kerja Islam berpengaruh positif dan signifikan terhadap loyalitas pegawai (nilai signifikansi 0,002 < 0,05), begitu juga dengan upah (nilai signifikansi 0,017 < 0,05). Secara simultan, kedua variabel tersebut juga berpengaruh signifikan dengan nilai Fhitung sebesar 18,042 > Ftabel 3,09 dan signifikansi 0,000 < 0,05. Nilai koefisien determinasi (R²) sebesar 0,280 menunjukkan bahwa 28% variasi loyalitas pegawai dijelaskan oleh etos kerja Islam dan upah, sedangkan sisanya 72% dipengaruhi oleh faktor lain yang tidak diteliti dalam penelitian ini. Kata Kunci: Etos Kerja Islam, Upah, Loyalitas Pegawai
Implementation of Naïve Bayes for Public Sentiment Analysis on QRIS and GPN Digital Dominance through Instagram Nabilah, Laila; Setiawan, Kiki
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.337

Abstract

This study examines public sentiment toward the dominance of QRIS and GPN compared to Mastercard and Visa, using data collected from Instagram comments. Employing the Knowledge Discovery in Databases (KDD) methodology and the Naïve Bayes Classifier, the research analyzed 820 comments retrieved through automated scraping and processed using text mining techniques such as case folding, tokenization, stopword removal, stemming, and TF-IDF transformation. The model achieved an accuracy of 84.27%, a precision of 86.09%, a recall of 94.7%, and an F1-score of 90.21%, indicating strong reliability in identifying sentiment polarity. The analysis revealed that 76.5% of the comments expressed positive sentiment, highlighting users’ appreciation of QRIS and GPN for their convenience, speed, and accessibility across both micro and macro-scale transactions. Negative comments, representing 23.5%, centered on concerns about connectivity, data security, and trust in financial governance. These findings suggest that while QRIS and GPN have been widely embraced as efficient digital payment solutions, there remains a need for improved infrastructure, user education, and data protection. The study demonstrates the effectiveness of the Naïve Bayes algorithm for large-scale sentiment analysis in multilingual online environments and provides empirical insights for policymakers to strengthen Indonesia’s digital payment ecosystem.
Implementation of the Naive Bayes Method in Looker Studio for data on the achievement of Great IDN in IDN Akhwat School Akbar, Yuma; Az-Zahra, Haura Salsabila; Setiawan, Kiki; Fajri, Raisah
Indonesian Journal of Multidisciplinary Science Vol. 3 No. 11 (2024): Indonesian Journal of Multidisciplinary Science
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/ijoms.v3i11.981

Abstract

The IDN Hebat program is an important tool for schools to track and analyze student achievement data. However, with the targeted activities in the IDN program, challenges arise in managing and measuring achievement data efficiently. The research aims to develop a Web Cloud-based data management system for IDN Hisbat achievements at IDN Akhwat School by utilizing Google Looker Studio and the Naive Bayes Algorithm. The data source used in this study is by applying a classification dataset obtained from student achievement information data in the Great IDN Program. The results of this analysis show that the highest accuracy of teaching achievement fell on the status of exceeding the target with a percentage of 89%, and the highest class that placed the status above the target was class 9A with an average percentage of 35%. In addition, the results from this analysis can help coordinators and schools in planning more effective and strategic programs in the future. Overall, this study provides important benefits in improving the quality of teaching and student coaching, as well as supporting data-driven decision-making. This study is expected to enhance the efficiency, accuracy, and effectiveness of managing student achievement, while also supporting the attainment of optimal educational goals for each student to achieve extraordinary results.
Analysis of Music Features and Song Popularity Trends on Spotify Using K-Means and CRISP-DM Marlia, Sari; Setiawan, Kiki; Juliane, Christina
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i2.3757

Abstract

Spotify, known as one of the best music streaming platforms, has played an important role in changing how listeners access, enjoy and interact with music. With millions of songs and extensive user data, Spotify provides an opportunity to understand listener behavior and the factors that contribute to a song's success and popularity. This research aims to examine the relationship between music features and the popularity of songs on the Spotify music platform by analyzing SSE values, Euclidean distance values, and cluster center values on the dataset attributes loudness, danceability, and energy. The framework used in this research is CRISP-DM (Cross-Industry Standard Process for Data Mining). The K-Means clustering algorithm and the Weka data mining application are used to decipher the features that influence the success and popularity of songs on Spotify. The research results show that groups/clusters 1, 2, and 3 are groups/clusters with songs that have high, medium, and low loudness, danceability, and energy respectively. Popular songs on Spotify are currently increasingly focused on loudness, danceability, and energy with a prominent trend, namely songs with high loudness, danceability, and energy are becoming more popular, while songs with low loudness, danceability, and energy are becoming less popular.
Penerapan Data Mining untuk Prediksi Pelanggan di PT. XYZ Menggunakan Algoritma Linear Regression: Application of Data Mining for Customer Prediction at PT. XYZ Using Linear Regression Algorithm Ramdhani, Fauzi; Setiawan, Kiki
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1217

Abstract

Dengan perkembangannya kegiatan export dan import dalam negeri terutama di daerah DKI Jakarta, membuat persaingan bisnis terutama dalam bidang depot kontainer makin marak. Sehingga menyebabkan dampak volume bongkar di dalam depot kontainer yang membuat pelanggan atau liner dapat berpindah ke kompetitor. PT. XYZ bergerak di bidang bisnis depot kontainer. PT. XYZ juga  menyediakan layanan pembayaran untuk memudahkan transaksi pelanggan agar dapat dilakukan Dimana saja sebagai salah satu cara untuk meningkatkan Strategi pemasaran dalam system ini mempunyai sekumpulan berbagai fitur untuk memudahkan transaksi. Penelitian ini dilakukan dalam rangka membantu tim Marketing untuk dapat mengetahui hasil prediksi transaksi pelanggan sehingga dapat menyiapkan strategi pemasaran yang lebih baik dalam menghadapi era kompetisi ini. Dalam kasus ini metod linear regresi dalam penambangan data adalah metod yang baik untuk melakukan prediksi. Software yang digunakan dalam metod Linear Regresi ini adalah RapidMiner dan menghasilkan nilai Root Mean Square Error (RMSE) sebesar 0.313 yang menunjukan proforma yang bagus dan hasil prediksi cukup akurat
Implementasi Sistem Informasi Layanan Ppdb Berbasi Web Di Smk Yadika 9 Kota Bekasi Tutuarima, Chelvin Joines; Setiawan, Kiki; Asyrofie, Maulana Azhar; imam, imam
Jurnal Ilmiah Wahana Pendidikan Vol 10 No 15 (2024): Jurnal Ilmiah Wahana Pendidikan 
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.13769131

Abstract

Abstrak- Pendidikan berperan penting dalam membentuk generasi muda sebagai pilar masa depan bangsa. SMK Yadika 9, komitmen pada pendidikan berkualitas, terus meningkatkan layanan, termasuk dalam PPDB. Di era digital, penggunaan Sistem Informasi Layanan PPDB web diperlukan untuk efisiensi dan transparansi informasi bagi calon siswa dan orang tua. Penelitian ini mengidentifikasi permasalahan sistem web PPDB di SMK Yadika 9 Kota Bekasi dan memberikan solusi. Metode penelitian deskriptif kualitatif. Data dikumpulkan melalui observasi, wawancara, dan studi pustaka. Hasil menunjukkan beberapa permasalahan seperti kesalahan, informasi kurang lengkap, dan interaksi sekolah yang kurang. Solusi yang diusulkan antara lain upgrade sistem dan hardware, penyajian informasi yang jelas, dan peningkatan interaksi sekolah dengan calon siswa. Diharapkan implementasi solusi dapat membuat sistem PPDB di SMK Yadika 9 Kota Bekasi lebih efektif dalam melayani calon siswa dan orang tua.
Perbandingan Metode Naïve Bayes dan Support Vector Machine untuk Klasifikasi Sentimen Ulasan Wisatawan: Studi Kasus Mulia Resort Nusa Dua Bali Setiawan, Kiki; Mu'asyir, Humam
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 2 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i2.5416

Abstract

The tourism industry requires systems that efficiently capture tourist perceptions. Online reviews on platforms like TripAdvisor provide valuable insights but are challenging to analyze manually due to their volume and diversity. This study develops a sentiment classification model for tourist reviews by comparing Naïve Bayes and Support Vector Machine (SVM). The dataset comprises public reviews of Mulia Resort Nusa Dua Bali, categorized as positive or negative. Text preprocessing includes tokenization, stopword removal, and TF-IDF transformation. Model performance is evaluated using accuracy, precision, recall, and F1-score. The study delivers a ready-to-use sentiment classification model and comparative performance analysis of both algorithms. Findings are expected to identify the more effective method for sentiment analysis of tourist reviews and provide a reference for building recommendation systems and strategic decision-making in the tourism sector.
Co-Authors Abdullah Aimar, Muqorrobin Akbar, Yuma Alafia, Evi Alifia, Tiara Ratu Amelia, Ika Amini, Tasya Aisyah Apriyanto, Kevin Jonathan Ardiyansyah, Ferry Aryanti, Putri Gea Asyrofie, Maulana Azhar Aula, Raisah Fajri Az-zahra, Haura Salsabila Azhar, Anisah Nurul Badzlin, Rosyiqoh Betty Yel, Mesra Christina Juliane, Christina Eldina, Ratih Fadillah, M. Rafli Fricco, Arjun Ghazi, Ahmad Imam Imam Kastum Lailany, Afyra Ar’bah Marindah, Marindah Marlia, Sari Miswanto Miswanto Mu'asyir, Humam Mufid, Ahmad Raihan Mumtahaen, Ikmal Nabilah, Laila Nauvalsalam, Nauvalsalam Nila Nopianti Oktavian, Nur Pratama, Yuliya Putri Purwasih, Intan PUTRI WULANDARI Qibthiyah, Mariyatul Raharjo, Pratama Putra Rahmawati, Dwita Ramdhani, Fauzi Regita, Anggit Nur Hannaa Rodhiyah Rodhiyah Rosidi, Raihan Putra Mohammad Salam, Nauval Saputra, Rizqi Kurniawan Saputry, Yulia Yanti Ayu Sarikah, Dede Sarimole, Frencis Matheos Sibarani, Julvan Marzuki Putra Sodik Sri Lestari Surapati, Untung Sutisna Sutisna, Nandang Syaputra, Ilham Adi Tundo, Tundo Tutuarima, Chelvin Joines Yrain, Beatrice Zakaria, Aditya Zidan, Muhammad Rohyan