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All Journal JSM (Jurnal SIFO Mikroskil) Jurnal Informatika dan Teknik Elektro Terapan E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Yayasan Cita Cendikiawan Al Khwarizmi Journal of Social Responsibility Projects by Higher Education Forum Jurnal Desain dan Analisis Teknologi
Sinaga, Frans Mikael
Univeritas Mikroskil, Medan
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SISTEM REKOMENDASI MUSIK DENGAN MULTIMODAL FUSION ATTENTIVE NETWORKS Chan, Jacky; Neveline, Neveline; Sherina, Sherina; Sinaga, Frans Mikael; Halim, Arwin
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i1.3732

Abstract

Musik merupakan salah satu media hiburan yang sering dimanfaatkan oleh masyarakat. Platform musik terus meningkatkan organisasi musik dengan menyediakan berbagai lagu untuk didengarkan oleh penggunanya. Secara umum sistem rekomendasi musik menggunakan metode Collaborative Filtering yang memiliki kinerja yang baik, tetapi metode ini memiliki masalah terhadap informasi yang tidak memiliki interaksi umpan balik antara item dan pengguna (cold-start). Sistem rekomendasi Content-Based diusulkan untuk mengatasi masalah tersebut yang akan difokuskan pada pendekatan multimodal fusion agar dapat mengoptimalkan hasil rekomendasi. Sistem rekomendasi ini mempertimbangkan dua pendekatan dengan fokus utama mengekstraksi fitur dari modalitas individu seperti judul lagu menggunakan model Word2Vec dan lirik menggunakan model BERT yang kemudian digabungkan melalui arsitektur deep learning untuk memprediksi rekomendasi lagu berikutnya. Hasil pengujian menggunakan black-box testing menunjukkan bahwa perangkat lunak secara fungsional mengeluarkan hasil yang sesuai dengan yang diharapkan dan sudah mencapai tujuan yang sesuai dengan kebutuhan pengguna. Penerapan algoritma rekomendasi akan diuji menggunakan confusion matrix dan memperoleh nilai akurasi yang cukup akurat yaitu sebesar 84%
Pelatihan Pembuatan Konten Pembelajaran Berbasis Video pada SMA Methodist 6 Sinaga, Frans Mikael; Irviantina, Syanti; Pipin, Sio Jurnalis
Journal of Social Responsibility Projects by Higher Education Forum Vol 4 No 3 (2024): Maret 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jrespro.v4i3.4588

Abstract

The use of information technology to support the teaching and learning process is considered important and necessary. It is not only the educators or teachers who are demanded to have the ability to use and utilize information technology, but students as well, in order to meet digital literacy competencies and be able to compete globally. At Swasta Methodist 6 High School, community service activities are carried out with the aim of improving students understanding in creating learning content using Canva and Wondershare Filmora tools. The availability of good facilities and infrastructure at school greatly assists students in utilizing technology to support the learning process. This activity was carried out for 2 days in the school's laboratory, and was attended by 43 students. The evaluation conducted with a pre-test and post-test showed an increase of 80% in the post-test for questions related to the use of Canva tools, and an increase of 60% in the post-test for questions related to the use of Wondershare Filmora tools.
Membangun Fondasi Pemrograman dengan Python pada SMA Swasta Methodist Tanjung Morawa Kelvin; Sinaga, Frans Mikael; Kurniawan, Heru; Winardi, Sunaryo; Saragih, Yuni Marlina
Dedikasi Sains dan Teknologi (DST) Vol. 4 No. 2 (2024): Artikel Riset Nopember 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v4i2.4875

Abstract

SMA Swasta Methodist Tanjung Morawa adalah salah satu sekolah swasta di bawah naungan Yayasan Methodist Kasih Imanuel Indonesia, yang berdiri sejak tahun 2008. SMA Swasta Methodist Tanjung Morawa memiliki ketertarikan terhadap teknologi robot yang merupakan teknologi yang saat ini sedang banyak-banyaknya diterapkan diberbagai sector. Salah satu bekal yang dapat dipersiapkan untuk para murid adalah pemahaman dalam menggunakan Bahasa pemrograman, sehingga para siswa nantinya dapat membangun sendiri berbagai instruksi dalam membuat robot atau bahkan sekedar program sederhana. Untuk mendukung keinginan tersebut, Fakultas Informatika Universitas Mikroskil menawarkan kegiatan berupa pelatihan pengenalan salah satu Bahasa pemrograman yaitu python. Kegiatan pelatihan ini berlangsung selama 1 hari dan dilaksanakan di laboratorium komputer Universitas Mikroskil. Selama kegiatan pelatihan ini para siswa akan mendengarkan pemaparan materi, mengerjakan latihan-latihan sederhana dan quiz pada akhir pelatihan sebagai evaluasi untuk menilai sejauh mana pemahaman siswa tentang bahasa pemrograman setelah pelatihan.
Peningkatan Penjualan Usaha Ekonomi Kreatif Akrilik Melalui Website Gunawan, Gunawan; Winardi, Sunaryo; Sinaga, Frans Mikael; Purba, Rizki Azzura Saifani; Tanidi, Davin; Filbert, Filbert
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 15, No 3 (2024): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v15i3.18438

Abstract

Usaha kerajinan akrilik “Sekuntoem”, yang dipimpin oleh Saudari Santi di Medan, didirikan pada tahun 2021 dan dijalankan oleh satu orang. Meskipun rata-rata melayani 20 transaksi per bulan, Sekuntoem menghadapi tantangan persaingan yang mengharuskannya untuk meningkatkan brand awareness. Solusi yang disediakan adalah pengembangan website yang akan menawarkan fitur custom design bagi pelanggan. Fitur unik ini memungkinkan pelanggan untuk mendesain produk akrilik mereka sendiri. Dengan pendekatan rekayasa perangkat lunak dan paradigma prototyping, aktivitas yang terlibat meliputi identifikasi kebutuhan, pengembangan wireframe, pembangunan prototipe, pengujian, dan implementasi, juga memberikan pelatihan kepada admin untuk menggunakan modul-modul website. Pemeliharaan website akan terus dilakukan untuk memastikan keberlanjutan pengembangan di masa depan.
Big Data Analytics for Predicting Depression Risk in Generation Z: Integrating Self-Organizing Maps and Long Short-Term Memory Sinaga, Joy Nasten; Nuraina, Nuraina; Sinaga, Frans Mikael; Kelvin, Kelvin; Nurhayati, Nurhayati
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38011

Abstract

Mental health issues among Generation Z are rising, with depression being one of the most significant challenges. Leveraging the capabilities of big data analytics and artificial intelligence, this study proposes a hybrid method combining Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) networks to predict depression risk based on behavioral data. The SOM algorithm is utilized for clustering high-dimensional input data to uncover hidden patterns, while the LSTM network is employed to capture sequential dependencies over time. Data were collected from various digital platforms, processed, and analyzed to train and validate the proposed model. Results show that the SOM-LSTM framework significantly improves the accuracy and reliability of early depression risk detection compared to conventional models. This study contributes a scalable and adaptable model for mental health prediction that can assist in timely interventions for Generation Z
A Smart Architecture for Stunting Prediction: Implementing the SOM–Voting Classifier on Healthcare Big Data Kelvin, Kelvin; Winardi, Sunaryo; Sinaga, Frans Mikael; Hardy, Hardy; Panjaitan, Erwin Setiawan; Wong, Ng Poi; Ferawaty, Ferawaty; Lim, Justine; Wijaya, Grace Putri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38000

Abstract

Childhood stunting is a persistent public health challenge in Indonesia. This study developed a predictive classification model using healthcare data from hospitals in Medan to enable early identification of at-risk children. A novel framework was proposed that integrated an unsupervised Self-Organizing Map (SOM) for feature engineering with a supervised Voting Classifier ensemble, which combined a Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB). The proposed framework achieved an accuracy of 100% on the test set, a substantial improvement over the 91.67% accuracy of the baseline Voting Classifier without SOM. While this result highlighted the model's high predictive potential, it must be interpreted cautiously, acknowledging the need for validation on more diverse datasets to ensure generalizability. The findings demonstrated that this hybrid machine learning approach can serve as a powerful decision-support tool, enabling proactive clinical interventions and aiding public health officials in strategically allocating nutritional resources to support Indonesia's national stunting reduction goals.
Analisis Keamanan Data Pasien pada Sistem Informasi Manajemen Rumah Sakit (SIMRS) Simanullang, Maradona Jonas; Aritonang, Mhd Adi Setiawan; Sinaga, Frans Mikael
Jurnal Desain Dan Analisis Teknologi Vol. 5 No. 1 (2026): Januari
Publisher : Aptikom Kepri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58520/jddat.v5i1.98

Abstract

Transformasi digital di sektor kesehatan mendorong rumah sakit untuk mengimplementasikan Sistem Informasi Manajemen Rumah Sakit (SIMRS) secara terintegrasi. SIMRS berperan penting dalam pengelolaan data pasien, termasuk rekam medis elektronik, administrasi pelayanan, serta pelaporan manajerial. Namun, meningkatnya ketergantungan terhadap sistem informasi juga meningkatkan risiko ancaman keamanan data pasien. Penelitian ini bertujuan untuk menganalisis keamanan data pasien pada SIMRS serta mengidentifikasi potensi risiko dan strategi mitigasi yang dapat diterapkan oleh rumah sakit. Metode penelitian yang digunakan adalah studi kasus dengan pendekatan deskriptif kualitatif, melalui observasi sistem, wawancara dengan tim teknologi informasi, dan analisis kebijakan keamanan informasi. Hasil penelitian menunjukkan bahwa ancaman keamanan data pasien meliputi akses tidak sah, kelemahan pengelolaan hak akses, serta kurang optimalnya penerapan standar keamanan informasi. Penelitian ini merekomendasikan penerapan kebijakan keamanan data yang komprehensif, peningkatan infrastruktur teknologi, serta penguatan sumber daya manusia untuk menjamin kerahasiaan, integritas, dan ketersediaan data pasien.
Analyzing Sentiment with Self-Organizing Map and Long Short-Term Memory Algorithms Frans Mikael Sinaga; Sio Jurnalis Pipin; Sunaryo Winardi; Karina Mannita Tarigan; Ananda Putra Brahmana
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3332

Abstract

This research delves into the impact of Chat Generative Pre-trained Transformer, one of Open Artificial Intelligence Generative Pretrained Transformer models. This model underwent extensive training on a vast corpus of internet text to gain insights into the mechanics of human language and its role in forming phrases, sentences, and paragraphs. The urgency of this inquiry arises from Chat Generative Pre-trained Transformer emergence, which has stirred significant debate and captured widespread attention in both research and educational circles. Since its debut in November 2022, Chat Generative Pre-trained Transformer has demonstrated substantial potential across numerous domains. However, concerns voiced on Twitter have centered on potential negative consequences, such as increasedforgery and misinformation. Consequently, understanding public sentiment toward Chat Generative Pre-trained Transformer technology through sentiment analysis has become crucial. The research’s primary objective is to conduct Sentiment Analysis Classification of Chat Generative Pre-trained Transformer regarding public opinions on Twitter in Indonesia. This goal involves quantifying and categorizing public sentiment from Twitter’s vast data pool into three clusters: positive, negative, or neutral. In the data clustering stage, the Self-Organizing Map technique is used. After the text data has been weighted and clustered, the next step involves using the classification technique with LongShort-Term Memory to determine the public sentiment outcomes resulting from the presence of Chat Generative Pre-trained Transformer technology. Rigorous testing has demonstrated the robust performance of the model, with optimal parameters: relu activation function, som size of 5, num epoch som and num epoch lstm both at 128, yielding an impressive 95.07% accuracy rate.
Co-Authors Adriansyah, R. A. Fattah Ananda Putra Brahmana Angkasa, Aswin Apriyanto Halim Arwin Halim Bate'e, David Belferik, Ronald Br Ginting, Geby Laurent Br. Sitorus, Rotua Eka Wati Carles Juliandy Chan, Jacky Chen, Kelvin Edmund, Edmund Fa, Farrell Rio Fandi Presly Simamora Felix Felix Ferawaty, Ferawaty Filbert Filbert, Filbert Gunawan Gunawan Gunawan, Erin Hardy Hardy Heru Kurniawan Heru Kurniawan Hui, David Christian Hutabarat, Yonatan Hutagaol, Grace Helena Irpan Pardosi Jerry Johnson Joy Nasten Sinaga Karina Mannita Tarigan Kelvin Kelvin Kelvin Liko, Frederick Lim, Justine Limbong, Mila Rachmafika Lubis, Muhammad Wahyu Pratama Manullang, Mangasa A.S. Marpaung, Dhea Romantika Maynius, Veilind Mhd Adi Setiawan Aritonang Michael Michael Muhammad Noor Hakim Neveline, Neveline Ng Poi Wong Nuraina, Nuraina Nurhayati Nurhayati Panjaitan, Erwin Setiawan Pipin, Sio Jurnalis Purba, Michael Emmanuel Purba, Rizki Azzura Saifani Rambe, Khairul Hawani Revelin, Dylan Ronsen Purba Saragih, Yuni Marlina Satria, Krismulia Wignya Sherina Sherina, Sherina Siahaan, Alfa Ulina Stefani Simanullang, Maradona Jonas Sinaga, Tetti Sintiya, Cindy Sio Jurnalis Pipin Sio Jurnalis Pipin Siregar, Roy Antoni Sitompul, Indra Maruba Situmorang, Angga Zefanya Sophya Hadini Marpaung Suhindra, Wahyu Sunaryo Winardi Susmanto, Susmanto Syahrul R, Syahrul Syanti Irviantina Tanidi, Davin Tanti Tanti Ulina, Mustika Vanessa Leonora Wijaya, Grace Putri Winardi, Sunaryo Wulan Sri Lestari