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All Journal Teknika TELKOMNIKA (Telecommunication Computing Electronics and Control) Primary: Jurnal Pendidikan Guru Sekolah Dasar Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal CoreIT Journal of Natural Science and Integration Seminar Nasional Teknologi Informasi Komunikasi dan Industri J I M P - Jurnal Informatika Merdeka Pasuruan Applied Information System and Management Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal IDAMAN (Induk Pemberdayaan Masyarakat Pedesaan) Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Zonasi: Jurnal Sistem Informasi INFORMASI (Jurnal Informatika dan Sistem Informasi) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Knowbase : International Journal of Knowledge in Database Malcom: Indonesian Journal of Machine Learning and Computer Science SATIN - Sains dan Teknologi Informasi
Pizaini Pizaini
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Pengaruh Agregasi Data pada Klasifikasi Sentimen untuk Dataset Terbatas Menggunakan SGD Classifier Fauzan Ray T; Surya Agustian; Febi Yanto; Pizaini
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

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

Abstract

Social media, especially Twitter or X, is a rich source of data for sentiment analysis. However, dataset limitation is a major challenge in utilizing machine learning, especially to produce fast and accurate sentiment analysis. This research applies data aggregation techniques to expand the training dataset and tests various preprocessing steps, such as cleaning, case folding, normalization, stemming, and lexicon-based methods. The classification method used is Stochastic Gradient Descent Classifier with text representation using Fast Text language model to generate word embedding. Lexicon-based preprocessing, particularly for emoji and emoticon handling, shows significant impact when data is added, as it is able to capture additional emotion and context that is often overlooked in conventional text analysis. Experimental results show that data addition and preprocessing optimization improved F1 Score from a baseline of 40% to 52.13%, surpassing the organizer which reached 51.28%. These findings emphasize the importance of data aggregation, preprocessing optimization, and parameter tuning using grid search in improving model performance on text sentiment classification with limited datasets.
Developing Programming Learning Media Using Scratch on the Concept of Buoyancy to Improve Computational Thinking in Primary School Hermita, Neni; Alim, Jesi Alexander; Almais, Agung Teguh Wibowo; Pizaini, Pizaini; Vebrianto, Rian; Thahir, Musa; Mandiro, Mulia Anton
Journal of Natural Science and Integration Vol 7, No 2 (2024): Journal of Natural Science and Integration
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jnsi.v7i2.32554

Abstract

The research focuses on the development of educational media using Scratch, a visual programming platform, to teach the concept of buoyancy and enhance computational thinking (CT) skills in primary school students. By adopting the 4D development model (Define, Design, Development, Dissemination), the study identifies challenges in traditional teaching methods, particularly the abstract nature of buoyancy, which often leaves students unengaged. The Scratch-based media addresses this by providing interactive simulations, allowing students to visualize and experiment with floating and sinking objects, thus making the learning process more engaging. The study involves designing a storyboard and flow of the media, followed by the development of simulations where students instruct sprites (characters) to test buoyancy. The media's effectiveness is validated by experts, who rate it based on display design, navigation, content relevance, interactivity, and technical suitability, with the overall results indicating that the media is valid and practical for use in educational settings. This approach not only helps students grasp scientific concepts but also builds their CT skills by integrating programming with science learning. The findings imply that such interdisciplinary tools can transform science learning by making abstract concepts more accessible and engaging, and encourage the development of both scientific and computational competencies in young learners.Keywords: buoyancy; computational thinking (ct); educational media; primary education; scratch programming
Evaluating primary students’ motivation and computational thinking in scratch-based learning: a confusion matrix analysis Neni Hermita; Jesi Alexander Alim; Agung Teguh Wibowo Almais; Pizaini; Rian Vebrianto; Musa Thahir; Tommy Tanu Wijaya; Mulia Anton Mandiro
Primary: Jurnal Pendidikan Guru Sekolah Dasar Vol. 13 No. 6 (2024): December
Publisher : Laboratorium Program Studi Pendidikan Guru Sekolah Dasar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33578/jpfkip-v13i6.p264-273

Abstract

This study examined the relationship between student motivation and computational thinking (CT) skills within a Scratch-based learning environment for primary school students. Utilizing a quantitative research design with a pretest-posttest framework, the research involved 28 primary school students engaged in a computational learning program centered on the Jumping Bean concept. A confusion matrix analysis was employed to assess the predictive relationship between motivation levels and improvements in CT skills. The results showed that motivation is a reliable predictor of CT gains, with high precision indicating that highly motivated students are very likely to demonstrate measurable progress. However, the recall score suggests motivation alone is not a conclusive factor, as some motivated students did not achieve the expected CT improvements. This implies that other instructional elements, such as prior knowledge, cognitive differences, teaching methods, and learning design, also significantly impact outcomes. The implications of this research suggest that educators should cultivate motivating learning environments to foster students’ CT skills effectively. Recommendations include integrating gamified elements and personalized feedback to enhance student engagement and motivation in computational learning contexts.
Implementasi Langchain dan Large Language Models Dalam Automatic Question Generation Untuk Computer Assisted Test Novri Rahman; Harahap, Nazruddin Safaat; Affandes, Muhammad; Pizaini
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.558

Abstract

The advancement of Artificial Intelligence (AI), particularly Large Language Models (LLM), presents new opportunities in transforming educational assessment systems. This study aims to implement the LangChain framework integrated with LLM for an Automatic Question Generation (AQG) system within a Computer Assisted Test (CAT) platform, using eleventh-grade Biology subject matter as a case study. The methodology includes data collection from PDF-based instructional materials, text embedding using Facebook AI Similarity Search (FAISS) as the knowledge base, and automatic question generation through the GPT-4o model. The system is developed using a microservices architecture comprising frontend and backend services built with the Next.js, FastAPI, and Express.js frameworks. System evaluation was conducted using the User Acceptance Test (UAT) and the DeepEval framework. The evaluation results show a teacher satisfaction rate of 92.7% and a positive response from students at 67.5%. Meanwhile, the DeepEval assessment reported average scores of 3,69% for hallucination, 97,44% for contextual precision, 83,30% for contextual relevancy, 70,63% for answer relevancy, and 92,47% for prompt alignment. These findings indicate that the integration of LangChain and LLM is effective in generating contextually accurate and relevant questions, although improvements are still needed in answer relevancy. This study is expected to provide an efficient solution for digital-based educational assessment and contribute to future developments in educational AI.
Pengelompokan Wilayah Bencana Banjir di Indonesia Menggunakan Algoritma K-Means Wenny Tarisa Oktaviany; Fitri Insani; Alwis Nazir; Pizaini
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.608

Abstract

Floods are one of the natural disasters that often occur in Indonesia, especially during the rainy season. This disaster is caused by various factors, both natural and caused by human activities, such as high rainfall, poor drainage systems, land conversion, and suboptimal spatial planning. The impact of floods is very detrimental, both physically and psychologically, including loss of life and damage to property. Therefore, a method is needed to group areas based on their level of vulnerability to flooding. This study aims to group flood disaster areas in Indonesia using the K-Means algorithm. The data used comes from the BNPB Geoportal covering flood events from January 2020 to December 2024, with a total of 7,487 events from 498 areas. Based on the test results obtained using the Silhouette Coefficient, it shows that 2 clusters were selected as the best number of clusters with a Silhouette Coefficient value of 0.8461 which is included in the strong clustering structure. Of the 2 clusters obtained, cluster 1 is a high-risk category consisting of 35 areas, while cluster 2 is a low-risk category consisting of 463 areas. The results of this study can provide information for related parties to improve the efficiency of flood disaster management.
Text to Speech Bahasa Jawa dialek Solo-Jogja dengan Metode VITS Wirdiani, Putri Syakira; Fikry, Muhammad; Yusra, Yusra; Yanto, Febi; Pizaini, Pizaini
TEKNIKA Vol. 19 No. 3 (2025): Teknika September 2025
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.16294499

Abstract

Pengembangan TTS di Indonesia masih berfokus pada Bahasa Indonesia dan bahasa asing, sementara bahasa daerah seperti Jawa dialek Solo-Jogja belum banyak tersentuh, padahal memiliki banyak penutur dan nilai budaya tinggi. Penelitian ini mengembangkan model TTS untuk dialek tersebut menggunakan metode Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech (VITS). Metode ini dipilih karena kemampuannya mengintegrasikan inferensi variasional, aliran normalisasi, dan pelatihan adversarial secara end-to-end, sehingga menghasilkan suara sintetis dengan kualitas lebih alami. Dataset berisi 450 pasangan teks dan audio dari penutur asli, dibersihkan manual dan disusun dalam format LJSpeech. Sebanyak 428 data digunakan untuk pelatihan dan 22 untuk evaluasi. Model dilatih menggunakan Coqui TTS di Google Colab dengan fonemizer eSpeak. Setelah pelatihan, model terbaik digunakan untuk menyintesis 50 kalimat uji yang dinilai oleh lima penutur asli menggunakan metode MOS. Rata-rata skor yang diperoleh adalah 4,088, melampaui standar minimum 4,0. Meski begitu, masih ada kekurangan dalam kejelasan fonem dan kealamian jeda. Hasil ini menunjukkan potensi besar TTS untuk pelestarian bahasa daerah dan pengembangan teknologi serupa untuk bahasa lokal lainnya.
PENERAPAN METODE LOGISTIC REGRESSION UNTUK KLASIFIKASI SENTIMEN PADA DATASET TWITTER TERBATAS Putri, Adilah Atikah; Agustian, Surya; Abdillah, Rahmad; Pizaini, Pizaini
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 1 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Januari 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v7i1.24804

Abstract

Kecepatan dan akurasi menjadi semakin penting dalam analisis sentimen publik, terutama di media sosial seperti Twitter, yang sering digunakan untuk menyampaikan opini terkait berbagai isu terkini. Penelitian ini mengaplikasikan metode Logistic Regression untuk klasifikasi sentimen pada dataset terbatas yang terdiri dari 300 sampel, yang dikategorikan menjadi sentimen positif, negatif, dan netral. Studi kasus mengeksplorasi respons masyarakat terhadap pengangkatan Kaesang Pangarep sebagai Ketua Umum Partai Solidaritas Indonesia (PSI) di Twitter. Data eksternal dari vaksinasi COVID-19 dan topik umum (open topic) digunakan dalam penelitian ini untuk meningkatkan proses klasifikasi. Metode TF-IDF digunakan untuk meningkatkan representasi teks. Grid Search digunakan untuk mengoptimalkan hyperparameter model. Evaluasi dilakukan menggunakan metrik F1-score untuk mengukur precision dan recall. Hasil baseline menunjukkan F1-score sebesar 40,83%, sementara berdasarkan hasil eksperimen yang dilakukan optimasi menghasilkan peningkatan hingga 52,68% dengan akurasi 61,76% pada eksperimen terbaik (C7). Penelitian ini menunjukkan bahwa metode Logistic Regression yang dioptimalkan dapat melakukan klasifikasi dengan dataset terbatas, yang relevan untuk analisis sentimen.
EXPERT SYSTEM TO DETECT ONLINE GAME ADDICTION FOR UNIVERSITY STUDENTS USING THE BACKWARD CHAINING AND CERTAINTY FACTOR APPROACHES Muslimin, Al’hadiid; Okfalisa, Okfalisa; Pizaini, Pizaini; Syafria, Fadhilah; Che Hussin, Ab Razak
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.4.1308

Abstract

Online gaming addiction harms students' physical health, mental well-being and academic performance. The addiction to playing online games has three categories, namely high, moderate and low, which are rarely known by the general public. The significant of knowledge acquisition on the addiction symptom and preventive activities forces the emergence of new idea on expert system identification platform. Therefore, this research aims to develop an expert system using the Backward Chaining (BC) and Certainty Factor (CF) approaches to detect the initial addiction level of online games for university students. Herein, the BC is used to identify the levelling of online game addiction based on the symptoms experienced by the user. There are thirty-three symptoms (G01-G33) provided through the thorough literature reviews and interviews with psychiatrics. Meanwhile, the CF is applied to calculate the level of certainty in determining the possibility of addiction describing in six scale level interpretation. As a result, the application of these two methods has effectively succeeded and reached proper accuracy in identifying the level of addiction of students towards their behavior on playing online games. The comparison of CF testing values between the system calculation and expert judgement shows the sophisticated result. Thus, this research can be utilized by the medical and psychiatric authorities, parents, and students in assessing their symptoms of addiction as an early warning in facing the possible risks arising from online game addiction.
Intrusion Detection System (IDS) Pada Snort Dengan Bot Telegram Sebagai Sistem Notifikasi Terhadap Serangan Syn Flood dan Ping Of Death Zuriati Ardila Safitri; Elin Haerani; Rometdo Muzawi; Muhammad Affandes; Pizaini
SATIN - Sains dan Teknologi Informasi Vol 10 No 1 (2024): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/stn.v10i1.1138

Abstract

Keamanan jaringan menjadi prioritas penting dalam era digital. Penelitian ini mengembangkan sistem Intrusion Detection System (IDS) berbasis Snort yang terintegrasi dengan bot Telegram untuk notifikasi real-time dan menggunakan kecerdasan buatan (AI) untuk mendeteksi serta mengelompokkan jenis serangan Syn Flood dan Ping of Death. Snort dikonfigurasi dengan aturan khusus untuk mendeteksi kedua jenis serangan ini. Bot Telegram digunakan untuk mengirimkan notifikasi langsung kepada administrator jaringan saat serangan terdeteksi. Hasil penelitian menunjukkan bahwa sistem ini mampu mendeteksi serangan dengan cepat, memberikan notifikasi real-time, dan mengelompokkan jenis serangan dengan akurasi tinggi. Integrasi ini meningkatkan efektivitas deteksi dan respons terhadap serangan jaringan, menawarkan solusi yang lebih aman dan efisien bagi organisasi.
End-to-End Text-to-Speech for Minangkabau Pariaman Dialect Using Variational Autoencoder with Adversarial Learning (VITS) Fakhrezi, Muhammad Dzaki; Yusra; Muhammad Fikry; Pizaini; Suwanto Sanjaya
Knowbase : International Journal of Knowledge in Database Vol. 5 No. 1 (2025): June 2025
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v5i1.9909

Abstract

Language serves as a medium of human communication to convey ideas, emotions, and information, both orally and in writing. Each language possesses vocabulary and grammar adapted to the local culture. One of the regional languages that enriches Indonesian as the national language is Minangkabau. This language has four main dialects, namely Tanah Datar, Lima Puluh Kota, Agam, and Pesisir. Within the Pesisir dialect, there are several variations, including the Padang Kota, Padang Luar Kota, Painan, Tapan, and Pariaman dialects. This study discusses the application of Text-to-Speech (TTS) technology to the Minangkabau language, specifically the Pariaman dialect, using the Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech (VITS) method. This dialect needs to be preserved to prevent extinction and supported through technological development that broadens its use. The VITS method was chosen because it is capable of producing natural and high-quality speech. The research stages include voice data collection and recording, VITS model training, and speech quality evaluation using the Mean Opinion Score (MOS). The final results show a score of 4.72 out of 5, indicating that the generated speech closely resembles the natural utterances of native speakers. This TTS technology is expected to support the preservation and development of the Minangkabau language in the Pariaman dialect, as well as enhance information accessibility for its speakers.
Co-Authors Abdillah, Rahmad Adha, Martin Aditya Dyan Ramadhan Afdhalel Vickro Agung Teguh Wibowo Almais Ahmad Fauzan Akhyar, Amany Albis Ya Albi Alwis Nazir Alwis Nazir Andrian Wahyu Arvansyah, M Afdhol Aslis Wirda Hayati Ayu Fransiska Bebi Oktaviani Che Hussin, Ab Razak citra ainul mardhia putri Deny Dewana Hastanto Dhymas Julyan Riyanto Eka Pandu Cynthia Elin Haerani Elvia Budianita Fadhilah Syafria Fahmi Kasri Fajar Febriyadi Fakhrezi, Muhammad Dzaki Faris Apriliano Eka Fardianto Faris Fauzan Ray T Febi Yanto Fitra Kurnia Fitri Insani Fitri Insani Fitri Insani Fitri, Dina Deswara Gusti, Siska Kurnia Haikal Zikri Hasibuan, Ilham Habibi Heru Sukoco Husnan Husnan Ibrahim Armadian Pujakesuma Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Jasril Jasril Jesi Alexander Alim Jesi Alexander Alim Kana Saputra S Khonofi, Khoidir Lestari Handayani Lola Oktavia m azwan M Wandi Dwi Wirawan M. Saski Mandiro, Mulia Anton Muhammad Affandes Muhammad Affandes Muhammad Fauzan Muhammad Fikry Muhammad Irsyad Muhammad Irsyad Muhammad Ridha Mulia Anton Mandiro Musa Thahir Muslimin, Al’hadiid Najmi, Risna Lailatun Nanda Sepriadi Nazir, Alwis Nazruddin Safaat H Neni Hermita Novi Yanti Novialdi T Novri Rahman Novriyanto Novriyanto Nur Iza Nuradha Liza Utami Okfalisa Okfalisa Okfalisa Okfalisa Putri, Adilah Atikah Rahmad Abdillah Rahmad Kurniawan Reski Mai Candra Reski Mai Chandra Rometdo Muzawi, Rometdo Roziana Roziana, Roziana Saktioto Saktioto Suci Rahayu Sugi Guritman Sukma Evadini Surya Agustian Suwanto Sanjaya Syarifuddin Syarifuddin Tarmizi, Veci Cahyono Teddie Darmizal Thahir, Musa Tommy Tanu Wijaya Umar Syarif Vebrianto, Rian Wenny Tarisa Oktaviany Wirdiani, Putri Syakira Yelfi Vitriani Yusra Yusra, Yusra Zuriati Ardila Safitri