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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Pendidikan Teknologi dan Kejuruan KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Techno.Com: Jurnal Teknologi Informasi Jurnal Sarjana Teknik Informatika Jurnal Informatika dan Teknik Elektro Terapan Jurnal Eksplora Informatika SINTECH (Science and Information Technology) Journal MULTINETICS Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pendidikan Sains dan Komputer Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Galuh International Journal of Community Service and Development
Ida Bagus Nyoman Pascima
Universitas Pendidikan Ganesha
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IMPLEMENTASI METODE SARIMA DALAM MEMPREDIKSI JUMLAH PELANGGARAN LALU LINTAS DI KABUPATEN BULELENG suryani, rika; Indradewi, I Gusti Ayu Agung Diatri; Pascima, Ida Bagus Nyoman
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8827

Abstract

Penelitian ini bertujuan untuk membangun model peramalan jumlah pelanggaran lalu lintas di Kabupaten Buleleng sebagai upaya mendukung perencanaan pengawasan yang lebih proaktif. Permasalahan penelitian didasarkan pada keterbatasan pengawasan di lapangan serta belum optimalnya pemanfaatan data historis, sehingga pihak kepolisian belum memiliki gambaran prediktif mengenai tren pelanggaran di masa mendatang. Metode yang digunakan adalah Seasonal Autoregressive Integrated Moving Average, yaitu metode prediksi deret waktu yang mampu menganalisis pola tren dan musiman. Data yang digunakan berupa data bulanan jumlah pelanggaran lalu lintas periode 2019 hingga 2024. Proses penelitian dilakukan mengikuti tahapan Cross-Industry Standard Process for Data Mining yang meliputi pemahaman masalah, pemahaman data, persiapan data, pemodelan, evaluasi, dan implementasi. Hasil penelitian menunjukkan bahwa model peramalan yang dibangun menghasilkan tingkat kesalahan peramalan sebesar 24,23% serta kemampuan mengikuti arah pergerakan data aktual sebesar 54,5%. Model juga mampu menghasilkan prediksi jumlah pelanggaran untuk 12 periode ke depan.
Analisis Sentimen Penggunaan Cekat.AI dalam Menggantikan Customer Service Menggunakan Logistic Regression dan TF-IDF Ardyaputra, Gede Yudha; Pascima, Ida Bagus Nyoman; Suputra, Putu Hendra
Journal of Computer System and Informatics (JoSYC) Vol 7 No 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i2.9292

Abstract

The rapid development of artificial intelligence has significantly transformed customer service systems, particularly through the use of chatbots to replace human customer service agents. Cekat.AI is one of Indonesia’s local AI-based chatbot innovations that has been increasingly adopted by companies. However, its implementation has generated diverse public reactions on social media platforms, especially X and TikTok. The main problem addressed in this study is how users perceive and respond sentimentally to the use of Cekat.AI as a replacement for human customer service, as well as the underlying factors influencing these ssentiments This study aims to analyze public sentiment using the Logistic Regression method with Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction on social media comments from X and TikTok. To address class imbalance in sentiment data, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The results indicate that negative sentiment dominates at 52.5%, followed by positive sentiment at 35.1% and neutral sentiment at 12.4%. The implementation of SMOTE significantly improved the Recall of the neutral class from 18.6% to 64.1%, with a cross-validation accuracy of 79.98%. Topic modeling further reveals that negative sentiment is primarily driven by automation anxiety and concerns over job displacement. These findings suggest that the main challenge in adopting Cekat.AI lies in social acceptance rather than technical performance. This study provides a dual contribution, namely technically proving the effectiveness of SMOTE in handling extreme imbalance in Indonesian text data, and practically revealing that public resistance to local AI is rooted in job displacement anxiety, not merely technical service aspects.
Perbandingan Performa LLaMA-2 dan GPT-3.5 Turbo Menggunakan Metode Retrieval Augmented Few-shot pada Analisis Sentimen I Wayan Adi Maha Wiguna; Ida Bagus Nyoman Pascima; Luh Putu Eka Damayanti
Jurnal Sarjana Teknik Informatika Vol. 14 No. 1 (2026): Februari
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v14i1.31872

Abstract

Large Language Models (LLM) memerlukan metode tambahan untuk optimasi pada tugas spesifik seperti analisis sentimen. Penelitian ini membandingkan performa GPT-3.5 Turbo dan LLaMA-2 melalui penerapan metode Retrieval Augmented Few-shot (RAFS) pada domain pariwisata, dengan skenario Zero-shot sebagai baseline. Hasil eksperimen menunjukkan bahwa LLaMA-2 mengalami peningkatan performa yang jauh lebih signifikan dibandingkan GPT-3.5 Turbo setelah penerapan RAFS. Akurasi LLaMA-2 meningkat dari 0,833 menjadi 0,862, sementara GPT-3.5 Turbo hanya meningkat tipis dari 0,851 menjadi 0,856. Perbedaan substansial terlihat pada metrik kelas minoritas; f1-score GPT-3.5 hanya naik dari 0,555 ke 0,572, sedangkan LLaMA-2 melonjak drastis dari 0,462 ke 0,676 dengan kenaikan presisi dari 0,395 ke 0,844. Secara head-to-head, LLaMA-2 terbukti sedikit lebih unggul dibanding dengan GPT-3.5 Turbo dalam menghasilkan klasifikasi yang tepat dan seimbang. Meskipun GPT-3.5 memiliki baseline awal yang lebih tinggi, LLaMA-2 menunjukkan kemampuan adaptasi dan skalabilitas yang lebih baik terhadap augmentasi konteks. Temuan ini menegaskan bahwa model open-source dengan dukungan RAFS mampu menyamai, bahkan melampaui model proprieter dalam menangani kompleksitas sentimen ulasan pelanggan.
Pengembangan Game Edukasi Mobile Sebagai Media Pendukung Peningkatan Kecerdasan Musikal Anak di Sekolah Menengah Pertama Pasaribu, Alesandro Obigael; Pascima, Ida Bagus Nyoman; Andayani, Made Susi Lissia
TIN: Terapan Informatika Nusantara Vol 6 No 9 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i9.9415

Abstract

This study was motivated by the suboptimal implementation of music education, particularly music notation material, in junior high schools, which has resulted in students’ low musical intelligence and the limited use of interactive technology-based learning media. This study aims to develop a mobile educational game as a learning medium to help students understand music notation and support the improvement of their musical intelligence. The method used in this study was Research and Development (R&D) with the Game Development Life Cycle (GDLC) development model, which consists of the stages of Initiation, Pre-Production, Production, Testing, Beta, and Release. The result of this study is a mobile educational game called NOMU, which has undergone validation and testing processes. The content expert validation showed a validity score of 100% with the criterion of “Highly Valid, or usable without revision,” while the media expert validation also obtained a validity score of 100% with the criterion of “Highly Valid, or usable without revision” after the refinement stage. In addition, the results of the user response test indicated a “Very Positive” qualification with a “Very Good” criterion. These results indicate that the developed mobile educational game is feasible to be used as a supporting medium for music learning and has the potential to support the improvement of students’ musical intelligence in junior high schools. The implications of this study suggest that mobile educational games can serve as innovative learning media that support the development of students’ musical intelligence and encourage the integration of digital technology in music education at the junior high school level.
Co-Authors Alfiansyah Andayani, Made Susi Lissia Ardyaputra, Gede Yudha Aristamy, I Gusti Ayu Agung Mas Arya Budhi Saputra, I Made Christina Purnama Yanti Darmika, I Wayan Dewi Agustini Dharsika, Eka Edi Wijaya, Ikomang Fitriani, Rani Ligar Gede Aditra Pradnyana Gede Arna Jude Saskara I Dewa Made Ditha Utama P I Gede Bendesa Subawa I Gede Mahendra Darmawiguna I Gusti Ayu Agung Diatri Indradewi I Gusti Lanang Agung Raditya Putra I Gusti Lanang Agung Raditya Putra I Komang Yuda Muliawan I Made Andhika Pramana Putra I Made Ardwi Pradnyana I Made Edy Listartha I Made Wedana Yuda Yana, Yuda I Nengah Eka Mertayasa I Putu Dedy Wira Darmawan I Wayan Adi Maha Wiguna Iswardani, Putu Risanti Kadek Ayu Ariningsih Ketut Agustini Krisnayana, I Komang Bayu Luh Putu Eka Damayanti M.Cs S.Kom I Made Agus Wirawan . Mertayasa , I Nengah Eka Ni Kadek Nopiani Ni Komang Tri Witari Ni Nyoman Sugihartini Ni Nyoman Utami Januhari Ni Wayan Risa Rahayu Saraswati Nyoman Arta Ada Pasaribu, Alesandro Obigael Pramudita, Putu Dimas Putra, I Komang Apriyatama Sukarma Putri, Ni Putu Meyka Pradnya Putu Hendra Suputra Putu Riesty Masdiantini, Putu Riesty Santoso, Erlinda Shirleen Sidiq, Maulana Sindu, I Gede Partha Sukarasa, Made Dwi Supriatman, Rian Dwicahya suryani, rika Thoyyibah, Luthfiyatun Wiguna, I Wayan Adi Maha Yogantara, Ketut Manik