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All Journal Tadris: Jurnal keguruan dan Ilmu Tarbiyah Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Faktor Exacta Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Eksplora Informatika JURNAL INSTEK (Informatika Sains dan Teknologi) Jiko (Jurnal Informatika dan komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) KOMPUTIKA - Jurnal Sistem Komputer JURNAL MANAJEMEN BISNIS JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jurnal Informatika Global Jambura Journal of Electrical and Electronics Engineering Jurnal Informatika dan Rekayasa Perangkat Lunak Klasikal: Journal of Education, Language Teaching and Science Jurnal Teknik Informatika (JUTIF) Mattawang: Jurnal Pengabdian Masyarakat PENGABDI: Jurnal Hasil Pengabdian Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) Brilliance: Research of Artificial Intelligence International Journal of Electronics and Communications Systems Jurnal Nasional Teknik Elektro dan Teknologi Informasi Online Learning in Educational Research Seminar Nasional Pengabdian Kepada Masyarakat Paradigma Edukasia: Jurnal Pendidikan dan Pembelajaran Teknovokasi : Jurnal Pengabdian Masyarakat Vokatek : Jurnal Pengabdian Masyarakat Seminar Nasional Hasil Penelitian LP2M UNM Information Technology Education Journal Jurnal Pengabdian Masyarakat Journal of Embedded Systems, Security and Intelligent Systems Ininnawa: Jurnal Pengabdian Masyarakat Journal of Security, Computer, Information, Embedded, Network and Intelligence System Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pengabdian Masyarakat dan Riset Pendidikan Journal of Progressive Information, Security, Computer and Embedded System Paramacitra : Jurnal Pengabdian Masyarakat Jurnal MediaTIK SISFOTENIKA Artificial Intelligence in Educational Decision Sciences Jurnal Sipakatau
Dewi Fatmarani Surianto
Universitas Negeri Makassar
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Klasifikasi Mahasiswa Berprestasi Menggunakan Fuzzy C-Means Dan Naive Bayes S.Intam, Rezki Nurul Jariah; Wulandari; Risal, Andi Akram Nur; Surianto, Dewi Fatmarani
Jurnal Ilmiah Informatika Global Vol. 15 No. 1: April 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i1.3666

Abstract

Success in the world of education is often associated with successful academic achievements. Therefore, processing information is very important to determine the selection of students who excel. However, study programs and student services often face difficulties in recognizing students who have achievements. In this research, outstanding students from the Faculty of Engineering, Makassar State University were determined using the Naive Bayes classification method combined with the Fuzzy C-Means (FCM) method to identify data patterns before classification. The criteria measured are GPA, achievements achieved, organizations attended, and the number of Semester Credit Units (SKS) that have been programmed. By using the Confusion Matrix, the evaluation results show an accuracy level of 98%, recall of 97%, precision of 100%, and F1-Score of 99%.
Analysis of Naive Bayes and Support Vector Machine Algorithms in Classification of Diabetes Cases Based on Lifestyle Factors Awalia, Andi Dio Nurul; Muhammad Fadhil Hani; Dewi Fatmarani Surianto
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9783

Abstract

The increase in diabetes mellitus cases globally, including in Indonesia, demands a more adaptive lifestyle-based risk prediction strategy. This study aims to evaluate and compare the efficiency of Support Vector Machine (SVM) and Naive Bayes in the diabetes risk classification process using a Hybrid clustering-classification approach . The data analyzed in this study were obtained from the Kaggle platform , with 8,500 data of diabetes patients analyzed based on the attributes of age, gender, and smoking history. The Clustering process was carried out using K-Means (k=3), resulting in three main groups with different lifestyle characteristics. The classification results showed that Naive Bayes provided stable performance with an F1-score of 0.975. Meanwhile, SVM excelled in terms of F1-score 98.3% and perfect AUC (1,000), and was able to classify all data in cluster C3 without error. However, SVM recorded a higher classification error in the majority cluster . This study concluded that SVM was superior by 0.8% over Naive Bayes . Naive Bayes is more suitable for balanced data, while SVM is effective for detecting patterns in minority groups. These findings support the use of a hybrid approach in lifestyle data-based diabetes early detection systems. Future research directions include integrating additional variables and ensemble techniques to improve model generalization.
Unraveling the Effects of AI Usage on Burnout among Programmers: An Apriori Algorithm Data Mining Approach Muhammad Fardan; Alwi, Ana Sulistiana; Darwing, Khalil Mubaraq; Surianto, Dewi Fatmarani; Putri Nirmala; Nurrahmah Agusnaya
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.9858

Abstract

Burnout is a growing problem across various industries, particularly among programmers who face high workloads and prolonged stress. In this digital era, the use of technologies such as AI can be a solution to reduce workloads and improve employee well-being. This study aims to identify how the use of AI can reduce burnout levels in programmers. The method used is a cross-sectional research design with data collection through a survey using the Google Form platform, and data analysis using descriptive techniques and the Apriori algorithm to find patterns in the relationship between the duration of AI use, workload, and burnout levels. The results show that the use of AI can help reduce burnout levels by lowering workloads, providing a basis for more effective interventions in the workplace.
Integrasi Metode Weighted Product (WP) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) untuk Pendukung Keputusan Penentuan Asisten Dosen Muthmainnah, Aindri Rizky; Pamput, Jessicha Putrianingsih; Adiba, Fhatiah; Surianto, Dewi Fatmarani; Nasrullah, Asmaul Husnah; Budiarti, Nur Azizah Eka
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The selection of teaching assistants requires an objective and effective decision-making system. This study designs a decision support system for selecting assistants in the Algorithm and Basic Programming course at JTIK, Universitas Negeri Makassar, by integrating the Weighted Product (WP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. WP assigns weights to evaluation criteria, while TOPSIS identifies the best candidates based on positive and negative ideal distances. The criteria include academic performance, communication, subject mastery, and teaching experience. Testing results show that the system produces consistent selections, aligned with manual calculations and recruitment outcomes, proving its validity and effectiveness in supporting the selection process.
Implementasi Kriptografi Vigenere Cipher untuk Keamanan Data Informasi Desa Irianti, Erva; Surianto, Dewi Fatmarani; Ainun Zahra Adistia; Muh. Juharman; Jumadil Ahmad Safi’i
Progressive Information, Security, Computer, and Embedded System Vol. 1, No. 1 Maret (2023)
Publisher : Sakura Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/pisces.v1i1.24

Abstract

Data security on village information is a very important issue because it contains a lot of village information data. Important data cannot be denied that data can be edited or changed by unauthorized persons. So the data becomes insecure. Therefore, a technique is needed to secure data, namely using the classic Vigenere cipher cryptography technique, to maintain the security and confidentiality of messages or information so that it cannot be read by anyone, a desktop-based application is designed using the Java programming language which is carried out on the Netbeans IDE software to implementing the vigenere cipher algorithm as a technique for securing village information.
Segmentation of Student Lifestyle Patterns for Insomnia Risk Identification Using the K-Means Algorithm Athiyyah Anandira; Azzah Ulima Rahma; Amanda Putri Lestari; Dewi Fatmarani Surianto
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.8683

Abstract

Insomnia is a common sleep disorder that occurs in college students due to unbalanced lifestyle patterns. This study aims to categorize students based on their lifestyle patterns and identify the risk of insomnia by applying the K-Means algorithm. Data were obtained from 198 active students of JTIK UNM batch 2021-2024 through a questionnaire. Five main variables were analyzed, such as sleep duration, caffeine consumption, gadget use, number of assignments per week, and hours of sleep. After the researchers transformed and normalized data, the clustering process had resulted in two clusters. The first cluster showed a higher risk of insomnia due to late bedtime and excessive gadget usage, while the second cluster tended to undergo a healthier lifestyle. The Davies-Bouldin Index value of 0.22 indicates superlative clustering qualities. This study provides an overview of student characteristics based on lifestyle and potential risk of insomnia.
Transfer Learning-Based CNN for Guava Fruit Disease Detection and Classification Azir Zuldani Pratama; Mustari Lamada; Surianto, Dewi Fatmarani
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.10153

Abstract

Guava (Psidium guajava L.) is a tropical plant from the Myrtaceae family and the Psidium genus that is susceptible to diseases such as anthracnose and scab, especially in humid environmental conditions. To accurately detect and classify these diseases, digital image-based technology is needed. However, previous studies still have limitations in dataset size, method variation, and model optimization. Therefore, a study was conducted with the title Guava Fruit Disease Detection and Classification System Using a Convolutional Neural Network (CNN) Based Transfer Learning Model. This study tested four Transfer Learning models, namely MobileNetV2, DenseNet169, VGG16, and EfficientNetV2B5. Based on the test results, the MobileNetV2 model with a combination of activation functions and optimizers (Swish, Swish, Adam) showed the best performance, having the fastest computation time, namely 10 minutes 17 seconds. This proves that the model built is not only superior in accuracy, but also efficient in execution time and can be applied to guava fruit disease detection and classification systems. These findings provide valuable insights into the MobileNetV2 method, combined with Swish, Swish, and Adam, as the best choice for classifying or detecting guava fruit disease levels compared to other methods. This approach can also lead to the development of a widely applicable web-based system for plant disease identification. This offers several benefits for farmers, including faster and more accurate disease detection, efficiency, and cost savings.
The Impact of Organizational Culture, Socioeconomic Status, and Previous Technology Experience on AI Learning: Mediating Role of AI Anxiety Among University Students S, Aprilianti Nirmala; Rahman, Edi Suhardi; Fakhri, M Miftach; Surianto, Dewi Fatmarani; Baso, Fadhlirrahman; Arifiyanti, Fitria; Amukune, Stephen
Tadris: Jurnal Keguruan dan Ilmu Tarbiyah Vol 10 No 2 (2025): Tadris: Jurnal Keguruan dan Ilmu Tarbiyah
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/tadris.v10i1.23939

Abstract

This study explores the influence of AI anxiety, organizational culture, socioeconomic status (SES), and previous technology experience on AI learning outcomes among university students. Utilizing a quantitative approach, data were collected from 368 students via an online survey. Structural equation modeling (PLS-SEM) was employed to analyze the data, revealing that AI anxiety, often considered a barrier, can enhance learning outcomes when managed effectively. The study further highlights the significant direct and indirect roles of organizational culture and SES in shaping AI learning, with AI anxiety acting as a mediator. The results underscore the importance of designing educational strategies that foster a balance between institutional support and individual autonomy, while addressing disparities in technological access across different social strata. These findings offer valuable implications for developing more inclusive, accessible, and effective AI educational practices. The findings suggest that while AI anxiety is generally seen as a negative factor, its potential to motivate students when effectively managed can be leveraged to enhance learning engagement. Furthermore, the research emphasizes the need for targeted interventions and policies that ensure equitable access to AI tools, particularly for students from lower socioeconomic backgrounds, to foster a more inclusive learning environment.
Analysis of Opinion Classification on Marriage Based on Support Vector Machine and Multi-Layer Perceptron Isma, Nur; Syahyaningsih, Lutfiah Tri; Surianto, Dewi Fatmarani; Fadilah, Nur
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.29616

Abstract

Marriage is an important aspect of social life that is influenced by cultural changes and public opinion, especially in the digital age. Public opinion on marriage is now widely disseminated through social media, both from traditional and modern perspectives. This study aims to classify public opinions on marriage using the Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) algorithms. The data used consists of 1,003 comments collected from social media. The study was conducted using two different approaches: stemming and data augmentation, which involves increasing the training data by modifying the original data to improve model performance. The results show that in the first approach, SVM achieved an accuracy of 77%, while MLP improved from 75% to 76% without stemming. In the second approach, data augmentation without stemming provided a significant improvement in accuracy, with SVM reaching 93% and MLP reaching 96%. Wordcloud visualization also highlights the importance of removing stopwords to reduce noise in the data. These findings indicate that data augmentation is an effective strategy for improving the performance of opinion classification models. This research contributes to the field of social sentiment analysis using machine learning approaches and is expected to serve as a reference for formulating policies aimed at improving marriage quality and family stability in Indonesia.
K-Means-Based Behavioral Segmentation of Social Media Users for Digital Communication Analysis in Indonesia Nurhidayat, Nurhidayat; Nur Fadillah; Dilla, Salsa; Syahrul, Syahrul; Surianto, Dewi Fatmarani
International Journal of Electronics and Communications Systems Vol. 5 No. 2 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i2.28749

Abstract

Social media has become a central part of daily life in Indonesia, yet the rapid growth of digital platforms presents challenges in analyzing large, unstructured user data. Responding to this issue, this study aims to cluster the behavioral patterns of Indonesian social media users using the K-Means Clustering algorithm, a data mining technique for unsupervised segmentation. Employing a quantitative approach, data were collected through an online questionnaire from 553 respondents aged 15–80 years. After data cleaning, normalization, and feature encoding, the optimal number of clusters was determined using the Elbow method and Silhouette Coefficient, resulting in two clusters with a Silhouette score of 0.177. Cluster 0 (303 respondents) represents highly interactive multi-platform users active on Instagram, TikTok, and YouTube for 3–6 hours daily, showing strong interest in entertainment and motivational content. Cluster 1 (250 respondents) includes more passive users, mainly on Instagram and TikTok, spending 3–4 hours per day with moderate engagement and a preference for motivational and self-development content. The findings demonstrate that K-Means Clustering effectively maps user behavior based on platform use, content preferences, motivations, and interaction patterns. The implications of these findings suggest that digital communication strategies need to be tailored to the characteristics of each user cluster so that the messages and content conveyed are more effective.
Co-Authors A. Arianugerah Ilham A. Arianugerah Ilham AA Sudharmawan, AA Abdal, Nurul Mukhlisah Abdul Muis Mappalotteng Abdul Wahid Adiba, Fathiah Adiba, Fhatiah Adnas, Diny Anggriani Agusyana, Nurrahmah Ahmar, Ansari Saleh Ainun Zahra Adistia Akbar, Mohammad Arsan Akbar, Muh. Arsan Akmal Hidayat Akmal Hidayat Akmal, Muhammad syafruddin Aksa, Muhammad Al Amanah, Muh. Nur Hidayat Alwi, Ana Sulistiana Amanda Putri Lestari Amiruddin Amri, Muh. Aidil Amukune, Stephen Andi Akram Nur Risal Andi Akram Nur Risal Andi Baso Kaswar Andi Baso Kaswar Andi, Tenriola Andika Isma Anwar Wahid Arifin, Afrisal Arifiyanti, Fitria Arifky, Reza Arsyad, Meisaraswaty Asis Nojeng Asri Ismail Athiyyah Anandira Awalia, Andi Dio Nurul Awaliah, Widiarti Awaluddin, Muhammad Ghazali Ayu Hasnining Ayu Safitri Ayu Safitri Azir Zuldani Pratama Azis, Putri Alysia Azzah Ulima Rahma B., Muhammad Fajar Bahar, Muhammad Mahdinul Bakri, Muh. Fajrin Bakri, Muhammad Fajar Baso, Fadhlirrahman Cahyana Resky, Andi Aulia Clarisha, Windi Darwing, Khalil Mubaraq Dary Mochamad Rifqie Della Fadhilatunisa Dhaffa Mulya Rahman Dilla, Salsa Dillah, Salsa Dwi Rezky Anadari Sulaiman Edi Suhardi Rahman Edy, Marwan Ramdhany FADIAH, NUR Fani, A. Astri Merilsa Fathahillah Fathahillah Fathahillah Fhatiah Adiba Fhatiah Adiba Firdaus Firdaus Firdaus Fitriani Dzulfadhilah Fitriyanty Dwi Lestary Fizar Syafaat Furqan Ali Yusuf Hanum Zalsabilah Idham Hardy M, Galang Hartini Ramli Helmy, Ahnaf Riyandirga Ariyansyah Putra Hidayat M., Wahyu Ilyas, Sitti Nurhidayah Indanasufya, Indanasufya Inez Sri Wahyuningsi Manguling Irianti, Erva Irwandi Ishaq, Muhammad Fahrul Rosi isma, Nur Ivan Fadillah Akram Iwan Suhardi Jariah S.Intam, Rezki Nurul Jariah, Rezki Nurul Jasruddin Jasruddin Daud Malago Jumadi Mabe Parenreng Jumadil Ahmad Safi’i Jusniar Khaerunnisa Nur Fatimah Syahnur Kurnia Prima Putra Lapendy, Jessica Crisfin Lavicza, Zsolt Lutfiah Tri Syahyaningsih M. Miftach Fakhri Makmur, Haerunnisya Mappangara, Surianto MARDIAH, AINA Muh. Juharman Muhammad agung Muhammad Akil Musi Muhammad Ansarullah S. Tabbu Muhammad Fadhil Hani Muhammad Fajar B Muhammad Fardan MUHAMMAD ILHAM Muhammad Nur Yusri Muhammad Rafli Aditya H. Muhammad Rakib Muhammad Try Dharsana Muharni Muharni Muhtadi, Nashiruddin Sahal Mulia, Musda Rida Muliadi Mustari Lamada Muthmainnah, Aindri Muthmainnah, Aindri Rizky Nafil Rizqullah Rajab Nafil Rizqullah Rajab Nashiruddin Sahal Muhtadi Nasrullah, Asmaul Husnah Natsir, Nasrah Ninik Rahayu Ashadi NIRMALA, PUTRI Nur Fadiah NUR FADILAH NUR FADILLAH Nur Risal, Andi Akram Nurhidayat Nurhidayat Nurjannah Nurrahmah Agusnaya Nurul Fadhilah Nurul Fadhillah Nurul Fadhillah S Nurul Mukhlisah Abdal Pamput, Jessicha Pamput, Jessicha Putrianingsih Parenreng, Jumadi M. Putri Nirmala Putri Zhachilia Susanto R, Mutmainnah Raden Mohamad Herdian Bhakti Rahman, Dhaffa Mulya Ramadhan, Haekal Febriansyah Rauf, Annajmi Resky, Andi Aulia Cahyana Rezki Angriani Pratiwi Kadir Rezki Nurul Jariah Rezky Anisar, Muh. Alief Rhania, Dhia Ridwan Daud Mahande Ridwan Daud Mahande Risaldi, Muhammad Rivai, Andi Tenri Ola Rosidah Rusli, Risvan S, Aprilianti Nirmala S, Muh. Rizal S, Nurul Fadhillah S.Intam, Rezki Nurul Jariah Sari Wulandari Sari, Putri Nanda Sasmita Sasmita Satria Gunawan Zain Setialaksana, Wirawan - Shabrina Syntha Dewi Shasa Inayah Vega Shasa Inayah Vega Siti Syarifah Wafiqah Wardah Soeharto Soeharto Sudarmanto Jayanegara Surianto, Dewi Fatmawati Syahrul Syahrul Syahrul Syahyaningsih, Lutfiah Tri Syam, Abd. Azis Syamsurijal Syamsurijal, Syamsurijal Tenriola, Andi Udin Sidik Sidin Wahid, M Syahid Nur Wahid, M. Syahid Nur Wahid, Yokogeri Abdullah Wahyu Hidayat Wahyu Hidayat M Wahyu Hidayat M WAHYUDI Warda Wahyuni Wardah, Siti Syarifah Wafiqah Wardani, Ayu Tri WULANDARI Wulandari Wulandari Zulfikar, Muh Ihsan Zulhajji, Zulhajji