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All Journal International Journal of Advances in Applied Sciences IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Pendidikan Fisika Undiksha Jurnal Pendidikan Teknologi dan Kejuruan Prosiding Seminar Nasional MIPA Wahana Matematika dan Sains Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Sistem dan Informatika Mimbar Pendidikan: Jurnal Indonesia untuk Kajian Pendidikan KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sinkron : Jurnal dan Penelitian Teknik Informatika JUTIK : Jurnal Teknologi Informasi dan Komputer JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer SINTECH (Science and Information Technology) Journal Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Serambi Engineering Jurnal RESISTOR (Rekayasa Sistem Komputer) International Journal of Community Service Learning Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Riset Informatika Jurnal Ilmiah Sinus Jurnal Teknologi Informasi dan Pendidikan Jurnal Mantik Jurnal Teknologi Informasi dan Terapan (J-TIT) JURNAL ILMU KOMPUTER INDONESIA Jurnal Teknologi Informasi dan Multimedia Bali Medika Jurnal Indonesian Journal of Electrical Engineering and Computer Science INFOKUM Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Locus Penelitian dan Pengabdian Malcom: Indonesian Journal of Machine Learning and Computer Science Bulletin of Network Engineer and Informatics (BUFNETS) INOVTEK Polbeng - Seri Informatika
I Gede Aris Gunadi
Universitas Pendidikan Ganesha
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

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Improving Butterfly Fish Image Classification Accuracy using HSV Feature Extraction and SMOTE-Based Data Balancing Putra, I Putu Arya; Wirawan, I Made Agus; Gunadi, I Gede Aris
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/vw52nn48

Abstract

Class imbalance in image data can reduce the accuracy of classification models, especially when the minority class data is much smaller than the majority class. This research focuses on enhancing the classification accuracy of butterflyfish images through the application of the Synthetic Minority Over-sampling Technique (SMOTE) for data balancing, combined with the K-Nearest Neighbor (KNN) algorithm utilizing HSV-based feature extraction. The datasets were collected in two conditions, namely conditioned (controlled background and lighting) and unconditioned (varied background and natural lighting). The research stages include preprocessing, HSV feature extraction, data balancing with SMOTE, and classification using KNN with various k values (3, 5, 7, 9) and cross-validation (k-fold 5 and 10). The experimental results show that SMOTE consistently improves accuracy on both types of datasets, with the best performance at k = 3 and k-fold = 10, namely 85.32% (conditioned) and 87.59% (unconditioned). This improvement occurs because a more balanced data distribution allows the model to optimally recognize features between classes. This study proves that the integration of SMOTE and KNN is effective in overcoming class imbalance in image classification, with potential applications in the fields of digital image technology, ecosystem management, and species identification.  
Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM Sisilia Fhelly Djun; I Gede Aris Gunadi; Sariyasa Sariyasa
Jurnal Teknologi Informasi dan Multimedia Vol. 5 No. 4 (2024): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.434

Abstract

The development of business strategies, particularly in the marketing of SMEs, requires the utilization of business intelligence as the foundation for objective decision-making. This research aims to develop a business intelligence scheme for SMEs and design targeted assistance strategies for SME support institutions. The implementation of business intelligence involves leveraging transactional data from SMEs to ascertain customer segmentation and correlating it with Customer Relationship Management (CRM) strategies. Transactional data is processed into a Recency, Frequency, Monetary (RFM) data model. Customer segmentation is achieved through a clustering process using the K-Means algorithm, and the results yield distinct profiles for SME customers. Evaluation processes are conducted to determine the optimal solution for the number of customer segments. Evaluation methods, including the Elbow Method, Silhouette Scores, and Davies–Bouldin Index, are employed to determine the optimum cluster. The evaluation results indicate that the optimum cluster is 3, with the best Silhouette Score being 0.548 and Davies–Bouldin Index at 0.76. The first customer segment exhibits the highest shopping frequency and monetary value, categorizing them as active and profitable customers. Special loyalty services are recommended for this segment. The second segment, despite having the largest number of customers, exhibits a shopping frequency of only 1-2 times, with an average recency of approximately the last 2 months. These customers require effective after-sales service. The third segment consists of customers who last shopped more than 6 months ago, making them a low-priority segment. Re-engagement strategies, such as email marketing, are suggested for this segment. Support institutions can focus on CRM assistance targeting these three identified segments.
Identifikasi Pola Komunikasi dan Kepribadian Siswa Sekolah Luar Biasa (SLB) Melalui Analisis Konten Media Sosial dengan Metode Anova dan K-Means Galih Cahyaningsih, Agung Ukki; Candiasa, I Made; Gunadi, I Gede Aris
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025126

Abstract

Penelitian ini dilakukan untuk menganalisis pola komunikasi siswa Sekolah Luar Biasa (SLB) melalui aplikasi WhatsApp serta mengidentifikasi kecenderungan kepribadian mereka berdasarkan aktivitas komunikasi digital. Metode yang digunakan adalah clustering K-Means dengan tiga indikator utama: waktu respons, panjang pesan, dan frekuensi pesan untuk mengelompokkan siswa ke dalam tiga tipe kepribadian, yaitu introvert, ambivert, dan ekstrovert. Data penelitian diperoleh dari 102 siswa SLB melalui hasil penambangan pesan WhatsApp. Kualitas klaster divalidasi menggunakan Davies-Bouldin Index (DBI) dengan nilai 0,9095, yang menunjukkan bahwa hasil pengelompokan cukup baik, dengan pemisahan antar klaster yang jelas dan tingkat homogenitas internal yang tinggi. Selain itu, dilakukan analisis korelasi menggunakan metode Spearman Rank-Order untuk mengetahui hubungan antara pola komunikasi dan kepribadian siswa. Hasil uji korelasi menunjukkan koefisien ρ sebesar 0,187 dengan nilai signifikansi 0,060, yang berarti terdapat hubungan positif namun tidak signifikan secara statistik. Dengan demikian, pola komunikasi digital dapat memberikan indikasi awal mengenai kecenderungan kepribadian siswa, tetapi belum dapat dijadikan dasar prediksi yang kuat.   Abstract This study was conducted to analyze the communication patterns of Special Needs School (SLB) students through the WhatsApp application and to identify their personality tendencies based on digital communication activities. The method used was K-Means clustering with three main indicators response time, message length, and message frequency to categorize students into three personality types: introvert, ambivert, and extrovert. The research data were obtained from 102 SLB students through WhatsApp message mining. The quality of the clusters was validated using the Davies-Bouldin Index (DBI), which produced a value of 0.9095, indicating that the clustering results were sufficiently good, with clear separation between clusters and high internal homogeneity. In addition, a correlation analysis using the Spearman Rank-Order method was conducted to examine the relationship between communication patterns and student personality. The results showed a correlation coefficient (ρ) of 0.187 with a significance value of 0.060, indicating a positive but statistically insignificant relationship. Therefore, digital communication patterns can provide an initial indication of students’ personality tendencies but cannot yet serve as a strong predictive basis.
Enhancement performance of the Naïve Bayes method using AdaBoost for classification of diabetes mellitus dataset type II I Gusti Agung Putu Mahendra; I Made Agus Wirawan; I Gede Aris Gunadi
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp733-742

Abstract

In using technology, especially in health sciences, machine learning modeling can make it easier to predict disease treatment. Naïve Bayes optimization with AdaBoost is needed because even though Naïve Bayes has the advantage of minimal parameters, its accuracy is susceptible to too many features. AdaBoost is used to overcome sensitivity to an excessive number of features and optimize its ability to handle complex datasets. This research aims to analyze the classification results of the Naïve Bayes method with the help of the AdaBoost method. This data comes from Community Health Centers I, II, and III Mengwi District, Bali Province patient medical records. The classification process uses the Naïve Bayes method and Naïve Bayes with AdaBoost, which is then evaluated using a confusion matrix. Two scenarios were used in testing: Naïve Bayes and AdaBoost-based Naïve Bayes. The algorithm is implemented on the dataset and tested directly using cross-validation. The evaluation results show that the Naïve Bayes method experienced an increase in accuracy of 5.92% at 5-fold and 5.93% at 10-fold on a dataset with 890 data. The addition of the AdaBoost method to diabetes classification has been proven to improve the accuracy performance of the Naïve Bayes method.
Evaluasi Sistem Informasi Rapor SP Menggunakan Metode Pieces dan Metode Topsis (Studi Kasus SMKN 1 Kuta Selatan) Ni Kadek Erna Supriathi; I Gede Aris Gunadi; Sariyasa Sariyasa
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 5 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i5.5614

Abstract

Penilaian hasil belajar dalam pendidikan bertujuan untuk menyeimbangkan antara peningkatan capaian hasil belajar siswa dan proses perkembangan pembelajaran, dengan memanfaatkan perkembangan ilmu pengetahuan dan teknologi yang semakin maju guna meningkatkan kualitas pembelajaran. Rapor Satuan Pendidikan (Rapor SP), sebagai sistem informasi yang digunakan pada tingkat sekolah dasar, menengah pertama, menengah atas, kejuruan, dan pendidikan khusus, belum dimanfaatkan secara optimal. Sebagian guru masih memerlukan pendampingan dalam penggunaan sistem tersebut, dan pada periode tertentu seperti akhir semester, aplikasi sering mengalami kendala, seperti akses yang lambat serta proses login yang kurang efisien. Penelitian ini bertujuan untuk melakukan evaluasi secara komprehensif terhadap permasalahan implementasi Aplikasi E-Rapor SP di SMKN 1 Kuta Selatan dengan menggunakan metode PIECES untuk mengidentifikasi kelemahan sistem, serta menentukan dan memprioritaskan rekomendasi solusi perbaikan yang paling efektif dan efisien menggunakan metode TOPSIS. Teknik pengumpulan data meliputi kuesioner dengan skala Likert, wawancara, dan diskusi yang dianalisis menggunakan metode PIECES dan metode TOPSIS. Hasil penelitian menunjukkan bahwa prioritas utama rekomendasi perbaikan ditetapkan pada tiga solusi, yaitu penerapan sistem pencadangan data (backup) mendekati waktu nyata (near real-time) (A2), penguatan sosialisasi pengisian data email untuk proses reset password (A13), serta penerapan validasi format input secara otomatis (A10). Hasil analisis juga menunjukkan bahwa perbaikan pada infrastruktur sistem (stabilitas, keamanan, dan kualitas data) merupakan prasyarat yang lebih mendesak dan efektif (prioritas A2) dibandingkan dengan solusi berupa bimbingan teknis atau sosialisasi semata.
Perencanaan Strategis Sistem Informasi dan Teknologi Informasi di SMA N 1 Singaraja Berbasis Ward and Peppard dan COBIT 2019 Lika Hanifah; I Gede Aris Gunadi; I Made Gede Sunarya
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 5 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i5.5844

Abstract

Perkembangan Sistem Informasi dan Teknologi Informasi (SI/TI) di sektor pendidikan menuntut institusi sekolah memiliki perencanaan strategis yang terarah agar mampu mendukung efektivitas proses bisnis dan peningkatan kualitas layanan. Penelitian ini bertujuan menyusun perencanaan strategis SI/TI pada SMA Negeri 1 Singaraja menggunakan metode Ward and Peppard serta mengevaluasi tata kelola SI/TI berdasarkan kerangka COBIT 2019. Metode penelitian yang digunakan adalah deskriptif kualitatif dengan pendekatan studi kasus melalui observasi, wawancara, dokumentasi, dan kuesioner kepada pihak manajemen sekolah serta pengguna SI/TI. Analisis dilakukan menggunakan SWOT, PEST, Value Chain, Porter’s Five Forces, Critical Success Factors, dan McFarlan’s Strategic Grid, sedangkan evaluasi tata kelola menggunakan domain APO02, APO03, APO06, dan APO07. Hasil penelitian menunjukkan bahwa SI/TI telah mendukung aktivitas akademik dan administrasi, tetapi masih memerlukan peningkatan pada integrasi sistem, pengelolaan sumber daya, kompetensi SDM, keamanan data, serta dokumentasi kebijakan tata kelola. Pemetaan McFarlan’s Strategic Grid menghasilkan portofolio aplikasi pada kuadran strategic, high potential, key operational, dan support. Evaluasi COBIT 2019 menunjukkan capability level secara umum berada pada level 2, sehingga diperlukan standardisasi dan pengukuran proses yang lebih baik. Penelitian ini menghasilkan rekomendasi strategi SI bisnis, strategi TI, strategi manajemen SI/TI, portofolio aplikasi, roadmap implementasi, serta peningkatan tata kelola agar pengelolaan SI/TI sekolah lebih efektif, terarah, dan selaras dengan kebutuhan organisasi.
PERFORMANCE ANALYSIS OF NAÏVE BAYES CLASSIFIERS BASED ON THE INFORMATION GAIN-BASED FEATURE SELECTION WITH MULTICOLLINEARITY ANALYSIS Luh Putu Risma Noviana Risma; I Gede Aris Gunadi; I Made Gede Sunarya
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April - September 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/732114

Abstract

This study aims to analyze the performance of the Naïve Bayes Classifier algorithm by comparing several feature selection methods, namely Information Gain-based feature selection, Multicollinearity-based feature selection, and a combination of Information Gain and Multicollinearity. The dataset used in this study consists of 337 toddler stunting cases obtained from Kintamani I and VI Public Health Centers. The experiment was conducted using four testing scenarios: (1) Naïve Bayes Classifier without feature selection, (2) Naïve Bayes Classifier with Information Gain feature selection, (3) Naïve Bayes Classifier with Multicollinearity feature selection, and (4) Naïve Bayes Classifier with a combination of Information Gain and Multicollinearity feature selection. All experiments used a data split of 70% training data and 30% testing data, while model performance was evaluated using a confusion matrix. In the Information Gain feature selection stage, several features achieved the highest gain values, namely BPJS with a gain value of 1.0, immunization with a gain value of 1.0, age with a gain value of 0.842, maternal pregnancy history with a gain value of 0.791, and smoking habits with a gain value of 0.756. These features were retained in the final combined model because they contributed the most to the stunting classification process. In addition to improving predictive performance, the combination of Information Gain and Multicollinearity was also able to reduce feature redundancy, resulting in a more stable classification model. The results showed that the accuracy of the Naïve Bayes Classifier without feature selection was 90.10%, the Naïve Bayes Classifier with Information Gain feature selection achieved 95.05%, the Naïve Bayes Classifier with Multicollinearity feature selection achieved 93.07%, and the Naïve Bayes Classifier with a combination of Information Gain and Multicollinearity achieved the highest accuracy of 96.04%. These findings indicate that the combination of Information Gain and Multicollinearity produced the best performance among all tested methods. In addition, a coefficient of determination (R Square) test was conducted using SPSS, resulting in a value of 0.577, indicating that 57.7% of stunting classification was influenced by independent variables such as age, BPJS, immunization, smoking habits, and maternal pregnancy history, while the remaining 42.3% was influenced by other factors outside the scope of this study. The results also indicate that the Naïve Bayes algorithm combined with Information Gain feature selection and multicollinearity testing can be used as a stable and effective approach for early stunting classification to support decision-making in public health services.
An Integrated Smart Village Information System for Digital Public Services and BUMDes Marketplace in Temesi Village Made Junindra Maha Arta Sang; I Gede Aris Gunadi; Gede Rasben Dantes
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/6kh1s185

Abstract

The digital transformation of village governance requires information systems that not only provide administrative services but also support transparency, community participation, and local economic activities. Previous village information systems mostly focused on basic administration or information publication, while integrated public services, community reporting, budget transparency, and village-owned enterprise marketplace features remain limited. This study aims to develop an integrated web-based Village Information System based on the Smart Village concept in Temesi Village, Gianyar Regency. The system was developed using the software development life cycle with the waterfall model, including requirements analysis, system design, implementation, testing, deployment, and maintenance. Data were collected through interviews, focus group discussions, and observations involving village officials and community representatives. The system was implemented using Laravel, MySQL, and a model-view-controller architecture. The developed system provides population data management, administrative service requests, service status tracking, village information, community complaints, budget transparency, village maps, galleries, and a village-owned enterprise marketplace. Functional testing showed that the main features worked according to the expected scenarios. Usability testing involving 50 respondents, consisting of 6 information technology experts, 4 village officials, and 40 village residents, obtained an average System Usability Scale score of 86.15, categorized as Excellent. These results indicate that the system is feasible, usable, and supports Smart Village implementation in Temesi Village.
Co-Authors ., Ketut Suma ., Putu Sonia Virgawati Pratiwi Adi Sista, Dewa Nyoman Agus Ariwanta, I Putu Yesha Agus Gunawan Agus Harjoko Agus Harjoko Agus Harjoko Ahmad Asroni Ahmad Asroni, Ahmad Anandita, Ida Bagus Gede Andiny T T Arditaloka, I Wayan Angga Ariasa, Komang Ariyani, Putu Wendy Artama, Made Bella Eka Wahyuningtias Cipta, I Putu Agus Eka Yatna Cokorda Oka Birawidya David Juli Ariyadi Dewa Gede Hendra Divayana, Dewa Gede Hendra Dewi Oktofa Rachmawati Dharmana, I Wayan Diatmika, I Ketut Agus Indra Dinata, I Made Anom Mahartha Erlangga, Anak Agung Gde Wahyu Sukma Fauzi, Muhammad Rizki Galih Cahyaningsih, Agung Ukki Gede Indrawan Gede Rasben Dantes Hajrin, M. Heryanto, I Wayan Agus I Gusti Agung Putu Mahendra I Ketut Paramarta I Made Candiasa I Made Gede Sunarya I Made Pradipta I Nyoman Sukajaya I Nyoman Wahyu Semeru Putra I Putu Agus Eka Yatna Cipta I Putu Aris Sanjaya I Putu Aris Sanjaya, I Putu Aris I Putu Dody Suarnatha I Putu Putra Damana I Wayan Agus Heryanto I Wayan Gede Suweca Antara I Wayan Pio Pratama I Wayan Rosiana I Wayan Sadia I Wayan Santyasa I Wayan Sukra Ida Ayu Mirah Cahya Dewi Jana Satvika, Gd. Aditya Kadek Yota Ernanda Aryanto Ketut Suma Ketut Suma . Ketut Suma . Komang Ariasa Komang Setemen Lika Hanifah Luh Joni Erawati Dewi Luh Putu Budi Yasmini Luh Putu Risma Noviana Risma Luh Rumni Oktaria M. Hajrin M.Cs S.Kom I Made Agus Wirawan . Made Artama Made Junindra Maha Arta Sang Made Wahyu Aditya Arta Made Windu Antara Kesiman Made Windu Segara Matius Ivan Bimasena Mimin Yeli Sholekah Moh. Heri Setiawan MS Prof. Dr. Ketut Suma . N Dinda Maharani Ni Kadek Erna Supriathi Ni ketut Lisa Maheni Ni Komang Rai Mirayanti NI LUH PUTU MANIK WIDIYANTI Ni Made Yeni Dwi Rahayu Ni Putu Eka Apriyanthi Nugraha, I Gede Pradipta Adi Nugraha, I Gusti Agung Satria Oktaria, Luh Rumni Pathni, Ida Ayu Wisma Anggaritha pramana, i gede pramana ade saputra Prof. Dr. Ketut Suma, MS . Putra, I Kadek Nurcahyo Putra, I Made Arya Adinata Dwija Putra, I Nyoman Wahyu Semeru Putra, I Putu Arya Putu Eka Parianthana Putu Sonia Virgawati Pratiwi . Rai Sujanem Risha, Nurfa Sandhiyasa, I Made Subrata Saputri, Ni Kadek Tesya Ari Sariyasa Sariyasa Sariyasa Sariyasa Sawitri D U Segara, Made Windu Sidik, Purnama Sisilia Fhelly Djun Sonia Dewi Parna.T Sri Hartati Suputra, I Putu Arsana Suryawan, I Made Yuda Sutarno, Erwan Sutarno, Erwan Suweca Antara, I Wayan Gede T, Andiny T U, Sawitri D Wardana, I Komang Tri Edi Wayan Eka Ariawan Yogi Duwi Antara