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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Telematika : Jurnal Informatika dan Teknologi Informasi INFORMAL: Informatics Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JUTIK : Jurnal Teknologi Informasi dan Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal EDUTECH Undiksha Jurnal Mantik UTAMAX : Journal of Ultimate Research and Trends in Education Indonesian Journal of Data and Science Jurnal Scientia JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) PROFICIO: Jurnal Pengabdian Masyarakat Cendikia : Media Jurnal Ilmiah Pendidikan JURNAL ILMIAH GLOBAL EDUCATION JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal Pengabdian Kepada Masyarakat Abdi Nusa Parta: Jurnal Pengabdian Kepada Masyarakat Jurnal Puan Indonesia Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Penjaminan Mutu IJOEM: Indonesian Journal of Elearning and Multimedia Jurnal Krisnadana Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka Journal of Social Work and Empowerment KOMET Jurnal ilmiah teknologi informasi Asia Educare: Journal Educational and Multimedia
Aniek Suryanti Kusuma
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Sistem Informasi Kalkulasi Bahan Baku Berbasis Mobile pada PT. Sari Burger Indonesia Welda, Welda; Kusuma, Aniek Suryanti; Nuarta, I Ketut Pasek
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 3 No 4 (2021): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1404.744 KB) | DOI: 10.33173/jsikti.113

Abstract

PT. Sari Burger Indonesia, with one of its branches, is located at Jl. Teuku Umar, Level 21 Shopping Mall, Denpasar Bali. In realizing the company's goals by simplifying the process of calculating raw materials to be purchased by the company, there is a manager or, more precisely, an assistant manager in charge of calculating the raw materials to be purchased. This system is made using Data Flow Diagrams (DFD) design; this system is made based on Android, using android studio software, and with the help of Firebase, which has many readily available features, besides that this system uses the SMA (Single Moving Average) method. , this method is used during the forecasting calculation process. With this system, the goal of calculating the number of raw materials to be purchased becomes easier. The calculation process that was previously manual can now be easier to use a computerized system based on Android because the data inputted will be stored in the system in performing calculations. In addition, calculations that have been saved can also be downloaded using a file with a pdf extension. The system can also forecast the remaining stock, which will be used in estimating the amount of stock remaining next month.
Evaluation of the Career Guidance Program with the Context, Input, Process, and Product (CIPP) Model of the Career Center Field at the Indonesian Institute of Business and Technology INSTIKI Ayu Gede Willdahlia; Aniek Suryanti Kusuma; Ni Kadek Nita Noviani Pande; Desak Made Dwi Utami Putra
Educare: Journal Educational and Multimedia Vol. 2 No. 02 (2024): Educare: Journal Educational and Multimedia, October 2024
Publisher : Sean Institute

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

Abstract

This study aims to evaluate the educational program using the CIPP (Context, Input, Process, Product) model to assess the career services provided by the Career Center at INSTIKI and to evaluate the effectiveness of these services. The effectiveness of career counseling services is measured by how well the program's objectives have been achieved. The research uses a qualitative approach with a descriptive-analytic method, applying the CIPP evaluation model to assess each component: Context, Input, Process, and Product in the implementation of the career counseling program. The data sources for this study are the Career Center and alumni of INSTIKI. Data collection techniques involved interviews and observations with the Career Center, while additional data were gathered through questionnaires and documentation. The collected data were analyzed using triangulation techniques. The research findings indicate that the implementation of career planning and evaluation, career development, graduate placement, tracer studies, language services, and language development have a positive impact on the career advancement of graduates.
Comparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTubeComparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTube Ni Wayan Indah Juliandewi; Aniek Suryanti Kusuma; Kompiang Martina Dinata Putri; I Gusti Agung Indrawan; I Gusti Ayu Agung Mas Aristamy
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.366

Abstract

The advancement of digital technology has increased public engagement in expressing opinions and responding to issues on social media platforms such as X and YouTube. A prominent topic of recent public debate concerns Danantara's management of state-owned banks. This study analyzes public sentiment regarding this issue by comparing the performance of the Naïve Bayes and Random Forest classification methods. A dataset comprising 25,565 entries was collected from both platforms between January 2025 and May 2025. The data underwent text pre-processing, labeling with the InSet Lexicon, and feature weighting using term frequency-inverse document frequency (TF-IDF). The dataset was split at 80:20, and class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE) prior to classification. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results demonstrate that Random Forest performed stably, achieving 84% accuracy both before and after sampling. In contrast, Naïve Bayes achieved 74% accuracy before sampling, which increased to 79% after sampling. These findings suggest that Random Forest is more robust to data imbalance than Naïve Bayes, which is more susceptible to bias toward the majority class.
Comparison of Naïve Bayes and SVM in Sentiment Analysis of ChatGPT for Learning on X and YouTube Ni Putu Eka Swari; Ni Wayan Jeri Kusuma Dewi; Ni Ketut Utami Nilawati; Aniek Suryanti Kusuma; Ni Luh Wiwik Sri Rahayu Ginantra
Indonesian Journal of Data and Science Vol. 7 No. 1 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i1.382

Abstract

The rapid development of artificial intelligence technology has encouraged users to actively express opinions on social media platforms such as X and YouTube, including discussions on the use of ChatGPT as a learning support tool. This study aims to analyze public sentiment toward the use of ChatGPT in learning contexts by comparing the performance of the Naïve Bayes and Support Vector Machine (SVM) classification methods. A total of 5,500 comments from platform X and 5,543 comments from YouTube were collected through a crawling process using relevant keywords during the period from January 2023 to December 2025. The data were preprocessed and labeled into three sentiment classes (positive, negative, and neutral) using a lexicon-based approach with the INSET Lexicon. Feature extraction was conducted using the Term Frequency–Inverse Document Frequency (TF-IDF) method, and the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results show that the SVM classifier consistently outperformed the Naïve Bayes method on both platforms. On platform X, SVM achieved an accuracy of 76.67%, while Naïve Bayes obtained 74.60%. On YouTube, SVM achieved an accuracy of 73.10%, significantly higher than Naïve Bayes at 62.04%. These findings indicate that SVM is more effective for sentiment analysis of social media data related to the use of ChatGPT in learning environments
Transformation of Electronics Learning in Higher Education through Augmented Reality to Improve Student Achievement Anak Agung Gde Ekayana; Aniek Suryanti Kusuma; Ni Nyoman Parwati
Jurnal Edutech Undiksha Vol. 13 No. 2 (2025): December
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jeu.v13i2.102904

Abstract

Electronics learning still faces challenges due to the dominance of lecture-based methods, leading to low learning outcomes. This study develops and evaluates a smartphone-based AR medium to improve achievement through interactive and immersive learning experiences. This research approach is based on the development model presented by Borg and Gall, but it has been tailored to the demands of the study to cover only five essential stages. The research design uses a one-group pretest-posttest model involving 30 students as participants. Data collection using questionnaires and tests. Analysis methods using validity, N-Gain test and T-test. The test results show that the media has a high level of validity, with a score of 0.88 from subject matter experts and 0.84 from media experts. Limited trial findings show that this learning medium is highly practical, with an achievement rate of 82.07%.  This percentage grew to 85.67% throughout the full implementation stage, indicating that the usage of this medium becomes more efficient and follows the learning goals as it is deployed more broadly. The effectiveness test using t-test analysis showed a statistically significant improvement in learning outcomes (p = 0.000), with an N-Gain score of 0.58, which is in the moderate category. These findings indicate that AR learning media can improve student engagement, understanding, and performance in electronics learning. The study is limited to electronics courses, with unresolved issues in device compatibility and digital literacy. Future research should expand AR use to improve long-term retention.
Educational Chatbot Based on Educational Technology in Improving Student Understanding of Algorithms and Programming Courses Aniek Suryanti Kusuma; Anak Agung Gde Ekayana; Desak Made Dwi Utami Putra
Jurnal Edutech Undiksha Vol. 13 No. 2 (2025): December
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jeu.v13i2.104417

Abstract

The advancement of educational technology has driven the integration of Artificial Intelligence (AI) into learning processes; however, Algorithm and Programming courses remain challenging for students due to their abstract nature and the demands of logical thinking and problem-solving skills. This study aims to develop and evaluate the effectiveness of an AI-based educational chatbot in improving students’ understanding and engagement. The research employed an experimental method with a pre-test and post-test design involving two classes of students as research subjects. Data were collected through achievement tests designed to measure conceptual understanding, algorithmic thinking, and problem-solving abilities. Data analysis was conducted using paired sample t-tests to examine differences in learning outcomes and N-Gain analysis to determine the level of learning effectiveness. The results indicate a significant improvement in students’ learning outcomes after the use of the chatbot, with learning gains categorized as high in both groups. These findings demonstrate that AI-based chatbots are effective in enhancing conceptual understanding, algorithmic thinking skills, and student motivation. Therefore, educational chatbots have important implications as innovative solutions for creating adaptive, interactive, and student-centered learning environments in higher education.
TRANSFORMASI PEMBELAJARAN IPA MELALUI MEDIA INTERAKTIF PENGENALAN TATA SURYA PADA SD PELANGI JIMBARAN Welda W; Aniek Suryanti Kusuma
Jurnal Pengabdian Masyarakat FKIP UTP Vol 7 No 2 (2026): PROFICIO : Jurnal Abdimas FKIP UTP
Publisher : PROFICIO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36728/.v7i2.6541

Abstract

Pembelajaran IPA di tingkat sekolah dasar masih banyak bertumpu pada metode ceramah dan buku teks sehingga fenomena abstrak seperti pergerakan planet sulit dipahami siswa. Kegiatan pengabdian ini merespons kondisi di SD Pelangi Jimbaran yang menunjukkan rendahnya pemahaman konsep tata surya, lemahnya keterlibatan siswa, serta menurunnya motivasi belajar sains. Tujuan kegiatan adalah mentransformasi pembelajaran IPA melalui pengembangan dan implementasi aplikasi media interaktif pengenalan tata surya. Pendekatan yang digunakan adalah kualitatif dengan empat tahapan, yaitu analisis kebutuhan, pengembangan media, implementasi melalui pelatihan guru dan siswa, serta evaluasi. Data dikumpulkan melalui observasi kelas, wawancara semi-terstruktur, lembar kerja pemahaman, dan dokumentasi. Hasil menunjukkan bahwa media interaktif mengubah pola interaksi kelas dari mendengarkan pasif menjadi eksplorasi aktif, meningkatkan rata-rata pemahaman dari 34,6 persen menjadi 82 persen, serta memperkuat kepercayaan diri guru dalam memanfaatkan media digital. Kegiatan ini berkontribusi pada model pembelajaran sains kontekstual berbasis teknologi yang dapat diadopsi oleh sekolah dasar lain.
Literasi Pemanfaatan Artifical Intelligence (AI) dalam Mendukung Pembelajaran Anak Sekolah Dasar I Gede Iwan Sudipa; I Nyoman Widhi Adnyana; Aniek Suryanti Kusuma; I Komang Arya Ganda Wiguna; I Putu Agus Eka Darma Udayana; I Putu Mahesa Kama Artha; Made Leo Radhitya; Bagus Kusuma Wijaya; Gusti Ayu Shinta Dwi Astari; Ni Putu Widantari Suandana; Ni Putu Eka Kherismawati
Journal of Social Work and Empowerment Vol 4 No 2 (2025): Vol 4 No 2 (2025): Journal of Social Work and Empowerment - (Februari - April 202
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/jswe.v4i2.808

Abstract

Dalam era digital yang terus berkembang pesat, pengintegrasian teknologi, terutama Artificial Intelligence (AI), dalam pendidikan menjadi hal yang sangat penting untuk mendukung proses pembelajaran. Di SD Negeri 1 Kukuh Kerambitan, meskipun sudah ada pembelajaran komputer dasar, siswa masih belum optimal dalam memanfaatkan teknologi informasi untuk mendukung pembelajaran. Kondisi yang ada menunjukkan rendahnya literasi digital siswa, terutama dalam penggunaan teknologi informasi yang lebih mendalam, seperti aplikasi pembelajaran berbasis AI yang dapat meningkatkan kemampuan mereka dalam memecahkan masalah dan mengerjakan tugas-tugas sekolah. Selain itu, penggunaan gadget oleh siswa lebih dominan untuk hiburan daripada sebagai alat bantu pembelajaran.Untuk mengatasi permasalahan ini, program Literasi Pemanfaatan AI dalam Mendukung Pembelajaran Anak Sekolah Dasar akan diterapkan. Program ini menggunakan pendekatan Transfer Knowledge, Technology Transfer (TT), dan Difusi Ipteks dalam pelatihan literasi digital yang bertujuan untuk memperkenalkan teknologi informasi dan aplikasi berbasis AI yang dapat membantu siswa dalam pembelajaran. Dengan memberikan pelatihan tentang pemanfaatan teknologi, khususnya aplikasi yang berbasis AI, siswa diharapkan dapat meningkatkan keterampilan mereka dalam menggunakan teknologi untuk mendukung pemahaman materi pelajaran serta mengerjakan tugas secara lebih efektif. Indikator keberhasilan dari program ini meliputi peningkatan kemampuan siswa dalam memahami dan mengoperasikan aplikasi teknologi, serta pengoptimalan penggunaan alat pembelajaran digital untuk mendukung pembelajaran di kelas dan di rumah.
Classifying Indonesian Batik Motifs by Region Using Swin Small Transformer Architecture Ida Bagus Ketut Sukanegara; Aniek Suryanti Kusuma; Putu Sugiartawan
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 4 (2026): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.314

Abstract

Batik plays a crucial role in Indonesian cultural heritage, with regional motifs encoding local philosophies, identities, and socio-historical contexts while also sustaining creative industries and tourism. Automated classification of batik by region can support documentation, education, and authentication, yet remains challenging due to visually overlapping patterns, high intra-class variability, and subtle inter-regional differences. Building on recent advances in Vision Transformers, this study investigates the Swin Small Transformer architecture for classifying Indonesian batik motifs into five regional categories: Jawa Barat, Jawa Tengah, Jawa Timur, Madura, and Yogyakarta. The proposed framework employs the swin_small_patch4_window7_224} model initialized with ImageNet-pretrained weights and fine-tuned on a curated regional batik dataset. The hierarchical shifted-window attention mechanism of Swin is leveraged to capture both local repetitive elements and broader compositional structures that characterize regional styles. Experimental evaluation on a held-out test set consisting of 80 images demonstrates outstanding performance. The model achieves perfect classification results with overall accuracy, macro-averaged precision, recall, and F1-score all reaching 1.0000. No misclassifications are observed across any regional category, indicating that the proposed architecture effectively learns discriminative representations of regional batik motifs. These findings suggest that hierarchical Vision Transformers can robustly model the nuanced visual cues underpinning regional identity in batik patterns and provide a strong alternative to conventional convolutional neural network approaches. Beyond batik classification, the proposed framework may be extended to other cultural-heritage textile applications, supporting digital preservation, educational initiatives, and large-scale documentation of traditional artistic assets.
Enhancing Rice Disease Classification Using CLAHE and Transfer Learning on Leaf Image Data Samuel Welson; Aniek Suryanti Kusuma; Putu Sugiartawan
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 4 (2026): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.315

Abstract

Rice foliar diseases pose a major threat to global food security by reducing yield and grain quality, motivating the need for scalable, objective, and automated diagnosis solutions. This study investigates the impact of Contrast Limited Adaptive Histogram Equalization (CLAHE) and transfer learning on the classification of three common rice leaf diseases—Bacterial Blight, Brown Spot, and Leaf Smut—from RGB leaf images. Using a dataset of 2,342 images split into training, validation, and test sets (80:10:10), we design a controlled experimental pipeline comprising four scenarios: with/without CLAHE, and with/without transfer learning. CLAHE is applied as a preprocessing step to enhance local contrast and lesion visibility under heterogeneous illumination and cluttered backgrounds, while transfer learning leverages ImageNet-pretrained convolutional neural networks fine-tuned for rice disease recognition. Models are trained and evaluated using accuracy, macro F1, and weighted F1 on a held-out test set. The combined CLAHE + transfer learning configuration achieves the best performance, with an overall accuracy of 0.94 and macro and weighted F1-scores of 0.94, substantially outperforming non-enhanced and non-transferred baselines. Qualitative analysis indicates improved separability between visually similar classes, particularly Brown Spot and Leaf Smut, under challenging imaging conditions. These findings underscore the effectiveness of integrating contrast enhancement with transfer learning for robust, field-oriented rice disease classification and highlight a practical pathway toward reliable image-based decision support in precision agriculture.
Co-Authors -, Muchsini Adi Surya Artayasa Afrilawati, Retika Anak Agung Gde Ekayana Aristamy, I Gusti Ayu Agung Mas Aryati, Komang Sri Aryawan, I Made Gitra Ayu Gede Willdahlia Ayu Gede Willdahlia Ayu Gede Willdahlia Ayu Gede Willdahlia Ayu Gede Willdahlia Ayu Gede Willdahlia Ayu Manik Dirgayusari Bagus Kusuma Wijaya Batubulan, Kadek Suarjuna Bevi Libraeni, Luh Gede Desak Dwi Utami Putra Desak Dwi Utami Putra Desak Made Dwi Utami Putra Desak Made Dwi Utami Putra Desak Made Dwi Utami Putra Desak Made Dwi Utami Putra Dewi, Ni Kadek Feby Puspita Dewi, Ni Wayan Jeri Kusuma Dirgayusari, Ayu Manik Eddy Hartono Gede Surya Mahendra Gusti Ayu Shinta Dwi Astari I Dewa Made Krishna Muku I Gede Andika I Gede Iwan Sudipa I Gede Made Yudi Antara I Gede Ratnaya I Gede Sujana Eka Putra, I Gede Sujana Eka I Gusti Agung Indrawan I Kadek Budi Sandika I Kadek Dwi Gandika Supartha I Kadek Dwi Gandika Supartha I Ketut Setiawan I Komang Arya Ganda Wiguna I Komang Juliana I Komang Sudarma I Made Agus Sudiartha I Made Candiasa I Made Sutajaya I Made Tegeh I Nyoman Agus Suarya Putra I Nyoman Jayanegara I Nyoman Widhi Adnyana I Putu Agus Eka Darma Udayana, I Putu Agus Eka I Putu Mahesa Kama Artha I Putu Yudiarta I Wayan Adi Saputra I WAYAN SUDIARSA I Wayan Sukra Warpala Ida Bagus Ketut Sukanegara Ika Amellia Rizanty Indra Pratistha Junantara, Argi Ketut Agustini Komang Sri Aryati Kompiang Martina Dinata Putri Kusuma Dewi, Ni Wayan Jeri M.Pd S.T. S.Pd. I Gde Wawan Sudatha . Made Leo Radhitya Mr Welda Muchsini - Ni Kadek Nita Noviani Pande Ni Kadek Nita Noviani Pande Ni Kadek Nita Noviani Pande Ni Kadek Nita Noviani Pande Ni Kadek Nita Noviani Pande Ni Kadek Nita Noviani Pande Ni Ketut Utami Nilawati Ni Luh Wiwik Sri Rahayu Ginantra Ni Made Mila Rosa Desmayani* Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Nyoman Parwati Ni Nyoman Parwati Ni Putu Eka Kherismawati Ni Putu Eka Swari Ni Putu Manik Ardiyanti Ni Putu Mitha Laraswati Ni Putu Widantari Suandana Ni Wayan Indah Juliandewi Ni Wayan Jeri Kusuma Dewi Nuarta, I Ketut Pasek Pande, Ni Kadek Nita Noviani Putra, Putu Satria Udyana Putu Ayu Febyanti Putu Gede Surya Cipta Nugraha Putu Sugiartawan PUTU SUGIARTAWAN Sadam Mubaraq Sadiawan, I Wayan Gede Samuel Welson Sunarya, I Wayan Sutarwiyasa, I Ketut W, Welda Wardani, Ni Wayan Wawan Sudata Welda Welda Welda W Willdahlia, Ayu Gede Willdahlia, Ayu Gede