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All Journal Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika CogITo Smart Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Journal of Education Technology JURTEKSI Building of Informatics, Technology and Science Infotekmesin Journal of Information Systems and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Pengabdian Mitra Masyarakat (JPMM) Electronic Journal of Education, Social Economics and Technology Bulletin of Social Informatics Theory and Application CSRID Southeast Asia Journal on Open and Distance Learning
Rujianto Eko Saputro
Universitas Amikom Purwokerto
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Penerapan Model Ensemble Learning dengan Random Forest dan Multi-Layer Perceptron untuk Prediksi Gempa Turino, Turino; Saputro, Rujianto Eko; Karyono, Giat
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 2 (2025): JPTI - Februari 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.667

Abstract

Penelitian ini mengusulkan model hybrid yang menggabungkan metode Random Forest (RF) dan Multi-Layer Perceptron (MLPRegressor) untuk memprediksi magnitudo gempa bumi. Model ini bertujuan untuk meningkatkan akurasi prediksi dengan memanfaatkan kekuatan kedua algoritma tersebut, yang masing-masing memiliki keunggulan dalam menangani hubungan non-linier dan mengenali pola kompleks dalam data seismik. Evaluasi model menggunakan tiga metrik utama, yaitu Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan R-squared (R²). Hasil evaluasi menunjukkan bahwa model hibrida ini mampu memprediksi magnitudo gempa dengan akurasi yang cukup baik, dengan MAE sebesar 0,0738, RMSE 0,1078, dan R² 0,4204. Penerapan praktis dari model ini sangat relevan untuk sistem peringatan dini gempa bumi yang dapat membantu masyarakat untuk mengambil langkah-langkah pencegahan, seperti evakuasi dan penguatan infrastruktur di wilayah yang berisiko tinggi. Penelitian ini juga membuka peluang untuk mengembangkan model lebih lanjut dengan memperkenalkan data seismik real-time, algoritma pembelajaran mesin yang lebih canggih, dan penggunaan data geofisik serta pengamatan satelit untuk meningkatkan akurasi prediksi gempa bumi di masa depan. Dengan terus melakukan inovasi, ada potensi untuk mengembangkan sistem prediksi gempa bumi yang lebih akurat dan dapat diandalkan, yang pada akhirnya dapat meningkatkan kesiapsiagaan dan ketahanan terhadap bencana alam.
Elevating Student Motivation: Constructing a Gamified Massive Open Online Courses using the MARC Framework Saputro, Rujianto Eko; Salam, Sazilah
Journal of Education Technology Vol. 8 No. 1 (2024): February
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jet.v8i1.67624

Abstract

Education is undergoing a profound revolution in the current era of digital technology, primarily due to the implementation of Massive Open Online Courses (MOOCs). These courses have facilitated worldwide accessibility to education of exceptional quality, transcending geographical and institutional limitations. This study aims to evaluates the implementation of gamification in Massive Open Online Courses (MOOCs) to enhance students' intrinsic motivation and graduation rates. Facing the challenge of low graduation rates in MOOCs, this study designed and implemented a Gamified MOOC (GMOOC) platform using a new gamification framework, MARC. Through an experimental method, this study involved 101 students and collected data through a questionnaire based on the Instructional Materials Motivation Survey (IMMS) and ARCS Model. The data was analyzed using Structural Equation Modelling (SEM), and the results showed that all MARC variables have high reliability and positively impact students' intrinsic motivation, with the Autonomy variable having the most significant impact. This study underlines the importance of a gamification framework in enhancing motivation and graduation rates in MOOCs, as well as the importance of considering the correct design elements and gamification components in the development of MOOCs. This study provides significant implications for developing and implementing effective and engaging MOOCs.
Penerapan K-Means Clustering Untuk Mengelompokan Tingkat Kemiskinan Di Provinsi Kalimantan Barat Cahyo, Samsul Dwi; Wahyuni, Irmawati Tri; Maharani, Revalyna Octavia; Nurfaizi, Maulana; Saputro, Rujianto Eko; Tarwoto, T
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.855

Abstract

This study aims to use the K-means clustering algorithm to categorize poverty levels in the West Kalimantan province. The data used for clustering represents poverty levels across four districts: Melawi, Kapuas Hulu, Sekadau, and Kayong Utara. The K-means clustering method is employed to group these districts based on similarities in their poverty levels. The clustering results reveal four distinct categories of poverty levels: Cluster 0 represents areas with very high poverty rates; Cluster 1 shows Melawi with a high poverty rate; Cluster 2 includes Sambas, Kapuas Hulu, and Sintang, with relatively low poverty rates; and Cluster 3 includes Landak, Sanggau, and Ketapang, with high poverty rates. The analysis reveals interesting patterns in the distribution of poverty across West Kalimantan, which can assist local governments in designing more effective policies for poverty reduction. This study makes a significant contribution to understanding poverty dynamics in West Kalimantan and provides a basis for more efficient decision-making in poverty alleviation efforts.
Program pendampingan penulisan ilmiah dan eksplorasi kesenjangan penelitian menggunakan teknologi kecerdasan buatan bagi Dosen Fakultas Ilmu Komputer Universitas Amikom Purwokerto Widiawati, Chyntia Raras Ajeng; Utomo, Fandy Setyo; Saputro, Rujianto Eko; Sarmini, Sarmini; Adiya, Az Zahra Dwi Nur; Ilham, Rifqi Arifin; Hartini, Sri
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 4 (2024): December
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i4.26890

Abstract

AbstrakPermasalahan yang dihadapi oleh mitra sasaran Fakultas Ilmu Komputer (FIK) Universitas Amikom Purwokerto adalah rendahnya jumlah dosen yang memiliki gelar doktor yang berperan penting untuk meningkatkan kualitas pendidikan dan penelitian. Berdasarkan data FIK, dari 72 dosen, hanya 6 yang memiliki gelar doktor. Rendahnya jumlah doktor ini disebabkan karena dosen kesulitan dalam menentukan tema penelitian yang tepat dan relevan dengan kepakaran mereka, kesulitan menemukan dan merumuskan kesenjangan penelitian, serta kesulitan dalam merumuskan inovasi dan kebaruan riset. Berdasarkan permasalahan tersebut, kami memberikan solusi menyelenggarakan program Bootcamp Doktoral: Penulisan ilmiah dan identifikasi kesenjangan penelitian menggunakan teknologi kecerdasan buatan. Target yang diharapkan dari program ini, yakni dosen di lingkungan fakultas ilmu komputer dapat meningkatkan kompetensi akademik, mengembangkan jaringan profesional, meningkatkan keterampilan penelitian dan penulisan publikasi, memperoleh motivasi dan inspirasi untuk studi lanjut S3, mengembangkan soft skill, serta mampu beradaptasi dengan tren teknologi terbaru. Berdasarkan target luaran yang telah ditetapkan, metode pengabdian masyarakat yang digunanakan dalam program ini mencakup 3 tahap utama, yaitu tahap persiapan kegiatan, implementasi kegiatan, dan pelaporan kegiatan. Hasil evaluasi pelaksanaan program pendampingan melalui umpan balik peserta pada hari selasa, 20 Agustus 2024 secara daring diperoleh hasil bahwa seluruh peserta webinar dapat memahami pengoperasian tools berbasis teknologi kecerdasan buatan untuk penulisan ilmiah, dan memahami etika penggunaan teks atau data dari hasil tools kecerdasan buatan dalam konteks penelitian. Kata kunci: pendampingan; kecerdasan buatan; penulisan ilmiah; penelitian; doktoral AbstractThe problem faced by the target partners of the Faculty of Computer Science Universitas Amikom Purwokerto is the low number of lecturers who have doctoral degrees who play an essential role in improving the quality of education and research. The low number of doctors is caused by lecturers having difficulty determining the correct and relevant research themes with their expertise, difficulty finding and formulating research gaps, and difficulty formulating innovation and research novelty. Based on these problems, we provide a solution to organize a Doctoral Bootcamp program: Scientific writing and identifying research gaps using artificial intelligence technology. The expected target of this program is that lecturers in the faculty of computer science can improve their academic competence, develop professional networks, improve research skills and publication writing, gain motivation and inspiration for further doctoral studies, develop soft skills, and be able to adapt to the latest technological trends. Based on the set output targets, the community service method used in this program includes three main stages: the activity preparation stage, activity implementation, and activity reporting. The results of the evaluation of the implementation of the mentoring program through participant feedback showed that all webinar participants were able to understand the operation of artificial intelligence technology-based tools for scientific writing and understand the ethics of using text or data from the results of artificial intelligence tools in the context of research. Keywords: mentoring; artificial intelligence; scientific writing; research; doctoral
Optimalisasi kemampuan menulis akademik melalui teknologi AI: kolaborasi Universiti Teknikal Malaysia Melaka dan Universitas Amikom Purwokerto Sarmini, Sarmini; Saputro, Rujianto Eko; Utomo, Fandy Setyo; Putranto, R. Vitto Mahendra; Filanzi, Shendy; Adiatma, Febriansyah Husni
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 4 (2024): December
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i4.26335

Abstract

Abstrak Kebutuhan untuk meningkatkan kemampuan mahasiswa dalam memanfaatkan teknologi terbaru, khususnya AI, dalam proses penulisan ilmiah semakin penting di era digital. Kegiatan pendampingan penulisan artikel ilmiah menggunakan kecerdasan buatan (AI) diselenggarakan oleh Pusat Studi Media, Game, dan Mobile Universitas Amikom Purwokerto, dengan narasumber Assoc. Prof. Ahmad Naim Che Pee dari Human-Centered Computing and Information Systems Lab (HCC-ISL), UteM dilaksanakan pada tanggal 1 Agustus 2024. Tujuan dari kegiatan ini untuk memberikan pemahaman serta keterampilan praktis bagi mahasiswa dalam penggunaan AI, khususnya Chat GPT, dalam menyusun artikel ilmiah, mulai dari penyusunan struktur, pengecekan tata bahasa, hingga pengelolaan referensi. Metode pelaksanaan kegiatan meliputi pre-test untuk mengukur pemahaman awal mahasiswa, pelatihan yang mencakup teori dan praktik penggunaan AI, post-test untuk menilai peningkatan pemahaman, serta sesi tanya jawab dan evaluasi. Hasil evaluasi menunjukkan peningkatan signifikan dalam pemahaman mahasiswa, yang tercermin dari data hasil post-test yang lebih baik dibandingkan pre-test. Selain itu, mahasiswa menyatakan bahwa pelatihan ini memberikan wawasan baru yang relevan untuk mendukung tugas akademik mereka. Kata kunci: pendampingan; kecerdasan buatan; chat GPT; mahasiswa; penulisan artikel. Abstract Enhancing students' abilities to utilize the latest technology, particularly AI, in scientific writing is becoming increasingly important in the digital era. A scientific article writing mentorship program using Artificial Intelligence (AI) was organized by the Media, Game, and Mobile Research Center at Universitas Amikom Purwokerto, with the guest speaker Assoc. Prof. Ahmad Naim Che Pee from the Human-Centered Computing and Information Systems Lab (HCC-ISL), UTeM, held on August 1, 2024. This activity aimed to provide students with practical understanding and skills in using AI, specifically Chat GPT, in composing scientific articles, from structuring the paper and grammar checking to managing references. The implementation method included a pre-test to measure students' initial understanding, training sessions covering theory and practical use of AI, a post-test to assess improvements, and a Q&A session and evaluation. The evaluation results showed a significant improvement in students' understanding, as reflected in the post-test data, which were better than the pre-test results. Students reported that this training provided new and relevant insights to support their academic tasks. Keywords: mentoring; artificial intelligence; chat GPT; students; article writing.
Optimizing Marketplace Registration Page Design with Predictive Heatmap Analysis Bagaskoro, Galih; Eko Saputro, Rujianto; Shouni Barkah, Azhari; Nanjar, Agi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14547

Abstract

Optimizing marketplace registration pages is crucial for improving user experience and conversion rates. This study evaluates the design of registration pages for four leading Indonesian marketplaces Tokopedia, Shopee, Blibli, and Lazada—using Predictive Heatmaps from UX Pilot alongside Heuristic Evaluation and Gestalt Principles. The analysis identifies key usability issues, such as distractions from branding elements, inconsistent visual hierarchy, and a lack of real-time validation and feedback mechanisms. Findings indicate that while branding elements effectively capture user attention, they often divert focus from essential features, a trend observed not only in these marketplaces but also in broader UI design contexts. such as Call-to-Action (CTA) buttons and registration forms. Shopee and Lazada successfully utilize high-contrast CTA buttons to direct user interaction, whereas Tokopedia and Blibli suffer from visual distractions caused by mascots and unnecessary decorative elements. Heatmap results also reveal inconsistent grouping of interface components, reducing page efficiency. To enhance user experience and conversion rates, recommendations include improving CTA button visibility through contrasting colors and strategic placement, minimizing decorative distractions, and implementing real-time validation and feedback. The application of Gestalt Principles further aids in optimizing interface organization by grouping related elements more effectively. This study underscores the importance of a structured design approach incorporating heuristic and predictive analytics to enhance the usability of online registration pages. Future research may explore the impact of interactive elements and A/B testing in refining registration interfaces.
Enhancing Sentiment Analysis Accuracy Using SVM and Slang Word Normalization on YouTube Comments Saputra, Alfin Nur Aziz; Saputro, Rujianto Eko; Saputra, Dhanar Intan Surya
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14613

Abstract

Sentiment analysis is a crucial technique in understanding public opinion, particularly on social media platforms such as YouTube. However, the presence of informal language, including slang words, poses significant challenges to accurate sentiment classification. This study aims to enhance sentiment analysis by implementing a Support Vector Machine (SVM) classifier combined with SMOTEENN data balancing techniques to address class imbalance issues. The research collects 3,375 YouTube comments on the movie Pengabdi Setan 2: Communion using the YouTube Data API. The preprocessing steps include text cleaning, tokenization, stopwords removal, stemming, and slang word normalization using kamusalay.csv to ensure standardization of informal expressions. The extracted features are represented using TF-IDF, and sentiment labeling is performed using VADER. Experimental results show that the SVM model achieves an accuracy of 98%, but struggles with detecting negative sentiments, as indicated by lower recall (38%) and F1-score (53%) for the negative class. Although the application of SMOTEENN improves data balance, further refinements, such as adjusting classification thresholds and integrating deep learning techniques, are necessary to enhance sentiment detection, particularly for informal and emotionally nuanced language. This study contributes to improving sentiment analysis models by demonstrating the effectiveness of slang word normalization in handling non-standard language variations. Future work will explore more sophisticated language models to enhance accuracy in sentiment classification.
Improving Dolan Banyumas App: A Design Thinking Approach to Enhance Tourism Services Indriyani, Ria; Saputro, Rujianto Eko
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.991

Abstract

The Dolan Banyumas application is a digital step to support tourism in Banyumas Regency. However, the results of observations and evaluations conducted show that the design of the user interface (UI) and user experience (UX) of this application is still less than optimal, with incomplete information, confusing navigation, and unattractive application design. This study aims to redesign the application using the Design Thinking approach, which consists of five stages: empathize, define, ideate, prototype, and test stages. Usability was assessed using the System Usability Scale (SUS) with a 10-question Likert scale survey distributed to 30 respondents. Evaluation results using the System Usability Scale (SUS) method showed an increase in the average score from 63 to 81.42, which classifies the app into the “Good” and “Acceptable” categories. Improvements include easier-to-use navigation, more complete tourist information, and the addition of new features such as ticket booking and bus tour maps. The user satisfaction rate increased from 60% to 87%, while efficiency rose by 30%. Based on Net Promoter Score (NPS), the app is categorized as “Promoter.” The Design Thinking approach proved effective in improving the quality of user experience.
Eksplorasi Model Hybrid Transformer-Latent Semantic Analysis (LSA) Untuk Pemahaman Konteks Teks Berita Berbahasa Indonesia Sofa, Nur; Utomo, Fandy Setyo; Saputro, Rujianto Eko
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 5 (2025): JPTI - Mei 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.662

Abstract

Kemajuan teknologi informasi meningkatkan konsumsi berita digital, menuntut sistem Natural Language Processing (NLP) yang efisien dalam memahami bahasa Indonesia. Namun, kompleksitas morfologi bahasa Indonesia menyulitkan model NLP konvensional dalam menangkap makna semantik secara akurat. Model deep learning seperti Transformer unggul dalam menangkap hubungan semantik lokal, sementara Latent Semantic Analysis  (LSA) memahami hubungan semantik global melalui reduksi dimensi. Namun, Transformer membutuhkan sumber daya komputasi besar, sedangkan LSA cenderung kehilangan konteks sintaksis. Penelitian ini mengusulkan model hybrid yang mengintegrasikan Transformer dan LSA untuk meningkatkan pemahaman teks berita Indonesia serta mengevaluasi performanya dibandingkan model individu dan deep learning yang lebih kompleks. Evaluasi menggunakan Accuracy, F1-Score, BLEU Score, ROUGE, dan Perplexity. Model hybrid mencapai akurasi 0.510760 dan F1-Score 0.520486, lebih baik dari LSA dan Transformer, tetapi masih tertinggal dari BERT dan GPT. Meski demikian, model hybrid lebih efisien secara komputasi dibandingkan model deep learning yang lebih kompleks. Penelitian ini berkontribusi pada pengembangan NLP bahasa Indonesia dengan pendekatan yang lebih ringan. Implikasi penelitian menunjukkan perlunya dataset lebih besar dan teknik embedding lebih maju. Penelitian selanjutnya dapat mengeksplorasi integrasi model hybrid dengan BERT atau GPT, serta teknik embedding lain seperti word2vec atau fastText untuk meningkatkan pemahaman semantik.
Technology Acceptance Model TAM using Partial Least Squares Structural Equation Modeling PLS- SEM Latif, Imam Sofarudin; Saputro, Rujianto Eko; Barkah, Azhari Shouni
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1104

Abstract

The rapid advancement of digital technologies necessitates a deeper focus on user acceptance and satisfaction, particularly within the framework of the Technology Acceptance Model (TAM), analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This systematic literature review examines 36 articles published between 2020 and 2025, revealing that factors such as trust, system quality, perceived enjoyment, service quality, and technological self-efficacy significantly influence user satisfaction. These external variables enhance the explanatory power of TAM, providing a richer understanding of user interactions with digital platforms such as e-commerce, e-learning, and mobile banking. PLS-SEM's ability to manage model complexity, non-normal data distributions, and interrelated constructs further validates its suitability for this research. The findings suggest that integrating these external factors improves both the theoretical and practical aspects of TAM in the context of technology adoption. Future research could explore additional industry-specific applications for emerging technologies.
Co-Authors Adam Prayogo Kuncoro Adam Prayogo Kuncoro Adiatma, Febriansyah Husni Adiya, Az Zahra Dwi Nur Afriansyah, Fery Aimah, Samsul Arif Mu'amar Wahid Aulia Hamdi Azhari Shouni Barkah Bagaskoro, Galih Berlilana Berlilana Cahyo, Samsul Dwi Chyntia Raras Ajeng Widiawati Damayanti, Wenti Risma Dani Arifudin Darmono Deasy Komarasary Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Ely Purnawati Ely Purnawati, Ely Embong Octavianto Fandy Setyo Utomo Fatudin, Arif Faturama, Rafi Febrianti, Diah Ratna Fery Afriansyah Filanzi, Shendy Giat Karyono Hasna Salsa Dhia hidayatulloh, hanif Ikmah Ikmah Ikmah, Ikmah Ilham, Rifqi Arifin Indriyani, Ria Irwansyah Munandar Ismail, Dimas Shafa Malik Junianto, Haris Kusuma, Bagus Adhi Latif, Imam Sofarudin Lughri Wijaya Pamungkas Maharani, Revalyna Octavia Maulana Baihaqi, Wiga Millatul Izza, Nia Mohd. Hafiz Zakaria Munandar, Irwansyah Nanjar, Agi Ndari, Arum Vika Nia Millatul Izza Novita Eka Ramadhani Nurfaizi, Maulana Octavianto, Embong Pandu W, Muhammad Arfianto Prasetyo, Agung Pungkas Subarkah Purwadi Purwadi Putranto, R. Vitto Mahendra Radeta Tea Makdatuang Ramadhan, Rio Fadly Ria Indriyani Rizqi Aulia Widianto Rohmah, Umdah Aulia Rosana Fadila Sari safitri feriawan, Titi Salam, Sazilah Salsa Dhia, Hasna Samsul Aimah Saputra , Dhanar Intan Surya Saputra, Alfin Nur Aziz Saputri, Inka Sari, Rida Purnama Sarmini Sarmini - Sarmini Sarmini Sarmini Sazilah Salam Serli, Serli Sofa, Nur Sri Hartini Subarkah, Pungkas Suliswaningsih, Suliswaningsih Syahputra, Akhmal Angga Tanzilla, Armeyta Putri Tarwoto, T Tea Makdatuang, Radeta Titi Safitri Maharani Toni Anwar Turino, Turino Wahyuni, Irmawati Tri Wenti Risma Damayanti Wiga Maulana Baihaqi Wijaya, Anugerah Bagus Yuli Purwati Yulianto, Koko Edy