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All Journal Tekno - Pedagogi : Jurnal Teknologi Pendidikan Teknika TELKOMNIKA (Telecommunication Computing Electronics and Control) 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 Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK)
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
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.
Klasifikasi Sentimen Masyarakat Terhadap Kaesang Pangarep pada Media Sosial Twitter/X Menggunakan MLP Classifier dengan Fitur FastText Tarmizi, Veci Cahyono; Agustian, Surya; Okfalisa, Okfalisa; Pizaini, Pizaini
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8815

Abstract

Social media has become a primary channel for the public to express their opinions and reactions toward various political developments in Indonesia. One of the prominent discussions revolves around Kaesang Pangarep’s appointment as the Chairman of the Indonesian Solidarity Party (PSI). This study aims to analyze and classify public sentiment regarding this issue by employing the Multi-Layer Perceptron (MLP) algorithm integrated with FastText-based text representation. The dataset was collected from Twitter using keywords such as “Kaesang PSI”, and was further expanded with additional data from general topics including Covid-19 and Open Topic, ensuring a balanced distribution across positive, neutral, and negative sentiment categories for a more comprehensive representation of public opinion. The model’s performance was evaluated through four metrics: accuracy, precision, recall, and F1 Score. The experimental results demonstrate that the MLP–FastText model achieved consecutive scores of 0. 5129 for F1 Score, 0. 6035 for accuracy, 0. 5319 for precision, and 0. 5996 for recall. These findings indicate that the combination of MLP and FastText effectively captures sentiment patterns within textual data, particularly in the context of unstructured and dynamic social media content, and performs well when enhanced with relevant external data augmentation strategies.
Penerapan Algoritma C4.5 Mengklarifikasi Penerimaan Bantuan Sosial Menggunakan Feature Selection M Wandi Dwi Wirawan; Siska Kurnia Gusti; Jasril Jasril; Pizaini Pizaini
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 1 (2023): September 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6653

Abstract

The Indonesian government's efforts to overcome poverty in Indonesia are through the Smart Indonesia Card (KIP) program which is carried out by the government in the form of providing assistance to underprivileged families. The main aim of distributing KIP assistance is to help send underprivileged children to continue their education, the difficulties found in receiving KIP are due to the large number of residents registering, as well as the data having several conditions, the limited time available in providing KIP by sub-district parties, the completion base is relatively low, therefore the provision of assistance must be right on target. Therefore, the aim of this research is to look for the most influential attributes in receiving KIP assistance in order to improve the results of the data verification process. After carrying out Feature Selection using Information Gain, the most influential attributes can be obtained. The influences are Number of Art, Number of Rooms, Cooking Room, Refrigerator, Motorbike. Therefore, we need to know some of the attributes that most influence the selection of KIP assistance so that we can get accuracy values from decision tree modeling using the C4.5 algorithm or decision tree. Test This experiment can produce a decision tree in which the Number of Art attribute is the most influential attribute with the success rate of KIP acceptance. This evaluation uses a confusion matrix to obtain an accuracy value of 98.21%, precision of 98.21%, recall of 99.48%.
Penerapan Seleksi Fitur Untuk Klasifikasi Penerima Bantuan Sosial Pangkalan Sesai Menggunakan Metode K-Nearest Neighbor Muhammad Fauzan; Siska Kurnia Gusti; Jasril Jasril; Pizaini Pizaini
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 1 (2023): September 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6654

Abstract

The inability to fulfill basic human needs is how poverty is defined. To address this issue, the indonesian goverment implements various social assistance programs, one of which is Kartu Indonesia Pintar (KIP), aimed at providing free education to children aged 7-18 who are economically disadvantaged. However, in the distribution of aid in the Pangkalan sesai sub-district, distributing officers often face challenges due to the high number of eligible recipients applying, complex data requierements, and limited time for the officers. Distributing this social assistance accurately is crusial. Therefore, this research aims to determine the accuracy value for the data of potential recipients of the Kartu Indonesia Pintar (KIP to enhance the data verification process’s outcomes. To tackle this issue, the research employs the K-Nearest Neighbor (K-NN) algoritm and also employs feature selection using Information Gain to reduce less influential attributes. The data used consists of 1998 records of KIP beneficiaries from the 2023 in excel format, with 33 attributes. After performing data cleaning an Information Gain-based feature selection, the dataset is reduced to 1675 records, with 5 selected attributes. The best classification result in this study is achieved with ratios of 7:3 and 8:2, and a value of k = 5, yielding the highest accuracy of 98,21%. The lowest accuracy is obtained using a ratio of 9:1 with the same k value when not using Information Gain, resulting in an accuracy of 89,82%.
Klasifikasi Sentimen Komentar Youtube Tentang Pembatalan Indonesia Sebagai Tuan Rumah Piala Dunia U-20 Menggunakan Algoritma Naïve Bayes Classifer Ilham Habibi Hasibuan; Elvia Budianita; Surya Agustian; Pizaini Pizaini
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7096

Abstract

Text mining is a method used to perform tasks such as document classification, clustering, information extraction, sentiment analysis, and information retrieval. The Federation Internationale Football Association (FIFA), the international football governing body, has designated Indonesia as the host country for the U-20 World Cup starting in 2019. Indonesia is expected to be the choice venue for the U-20 World Cup in 2021. However, due to the Covid outbreak -19, the World Cup was rescheduled and is now scheduled to take place in 2023. Indonesia officially relinquished its position as host on March 31 2023. One of the reasons is the many factions that oppose the presence of the Israeli national team in Indonesia. As a result, various public reactions responded to Indonesia's decision to cancel holding the U-20 World Cup, especially on the Narasi tv YouTube channel video entitled "The U-20 World Cup Failed to Be Held in Indonesia, Let's Look at it from Two Perspectives | Discussion". Since the video was uploaded until August 16 2023, the total comments generated were 4,629 comments. This research uses a Naïve Bayes classifier approach. Naïve Bayes Classifier (NBC) is a direct probabilistic classifier that exploits Bayes' Theorem under strong independence conditions. The tests carried out show that the model performance when using stopword removal and stemming techniques is superior in classifying classes in the dataset. The F1-Score is 59.70% and the Accuracy value is 63.43%. Furthermore, after identifying the most efficient model for applying naïve Bayes classification, evaluation was carried out on validation data resulting in an F1-Score of 58.72% and an accuracy rate of 61.65%. Classification analysis shows that Indonesian people have a negative view or are disappointed with the cancellation
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 Arif Marsal 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 Fitra Kurnia Fitri Insani Fitri Insani Fitri Insani Fitri, Dina Deswara Gusti, Siska Kurnia Haikal Zikri Heru Sukoco Husnan Husnan Ibrahim Armadian Pujakesuma Ilham Habibi Hasibuan Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Jasril Jasril Jesi Alexander Alim Kana Saputra S Khonofi, Khoidir Lestari Handayani Lola Oktavia m azwan M Wandi Dwi Wirawan M. Saski Mandiro, Mulia Anton Mery Berlian Muhammad Affandes Muhammad Affandes Muhammad Affandes Muhammad Azmi Muhammad Fauzan Muhammad Fikry Muhammad Irsyad Muhammad Irsyad Muhammad Ridha 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 Syakira Wirdiani Putri, Adilah Atikah Rahmad Abdillah Rahmad Kurniawan Reski Mai Candra Reski Mai Chandra Rometdo Muzawi Roziana Roziana, Roziana Saktioto Saktioto Suci Rahayu Sugi Guritman Sukma Evadini Surya Agustian Suwanto Sanjaya Syarifuddin Syarifuddin Tahir, Musa Tarmizi, Veci Cahyono Teddie Darmizal Thahir, Musa Umar Syarif Vebrianto, Rian Wenny Tarisa Oktaviany Yelfi Vitriani Yovita Yovita Yusra Yusra, Yusra Zuriati Ardila Safitri