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All Journal Dinamik Jurnal Pendidikan Fisika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Mantik Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Data Sciences Indonesia (DSI) Jurnal Indonesia Sosial Teknologi Jurnal Pengabdian Masyarakat dan Riset Pendidikan Repeater: Publikasi Teknik Informatika dan Jaringan AT-TAKLIM: Jurnal Pendidikan Multidisiplin
Siregar, Saut Dohot
Universitas Prima Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Aplikasi Objek Wisata Kota Medan dengan Menggunakan Mobile Positioning Data Yordan, Vedi; Polfan, Wilsen; Khavitanjali, Khavitanjali; Gari, Paskah Kurniawan; Siregar, Saut Dohot
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11464

Abstract

Tourism Object is a fundamental component in the tourism industry and one of the reasons visitors travel. The State of Indonesia is one of the various countries whose number of tourist objects is very large and varied. One of these attractions is natural tourism, for example, the sea, beaches, rivers, lakes, and mountains, as well as for the building objects including historical heritage sites, forts, museums and so on. The method used in this study uses qualitative research by applying the method of mobile positioning data to help the manager of tourist attractions promote the location of tourist attractions. The mobile positioning system model can be used to understand and determine the mobile position based on location coordinates. This mobile positioning system uses GPS technology, a tool to get or generate the coordinates of the position of the online map technology that Google provides to show the position that has been stored in the system. The research results from this Tourism Object application can detect the user's location according to the mobile positioning data method. This Android-based Tourist Object application can display a list of tourist objects according to the area occupied by the user. This tourist attraction application can add new tourist objects by admin. It is hoped that in the future this application can be run on the iOS system and can add images to new tourist attractions.
Breast Cancer Classification Through CT Scan Using Convolutional Neural Network (CNN) Loi, Anita; Panjaitan, Ruth N; Siregar, Saut Dohot; Simarmata, Allwin M
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13706

Abstract

A common disease suffered by Indonesian women is breast cancer. Early awareness of breast cancer is very important to minimize the negative impact and increase the chances of recovery for breast cancer patients. Breast cancer detection efforts using CT scan image technology. CT scan images provide a detailed picture of the internal structure of the breast, allowing the identification of pathological changes that may be early signs of breast cancer. The purpose of the study is to utilize CNN algorithm for breast cancer classification using CT scan images. The dataset used consists of three labels namely benign cancer, malignant cancer, normal. The three data sets consist of 1096 data. CNN is a type of algorithm in the field of artificial intelligence that has proven successful in pattern recognition on image data. The collected breast CT scan image dataset includes breast cancer and non-breast cancer cases. The data is used to train and test the CNN model. Furthermore, breast cancer classification through CT scans is carried out by applying the CNN method. The results of the research conducted obtained an accuracy of 97.26%. In Benign classification with precision 0.99 (99%), recall 0.96 (96%), f1-score 0.98 (98%), support 186, then Malignant classification with precision 93% or with points 0.93, recall 98% with points 0.98, and f1-score 96% with points 0.96, and support 202. The last is the normal classification with 99% precision with 0.99 points, 97% recall with 0.97 points, 98% f1-score with 0.93 points, and 269 support.
Diagnosa Jenis Mata Katarak Menggunakan Convolutional Neural Network Pratama , Jordan Putra; Simangunsong, Andri Daniel Martua; Siregar, Saut Dohot
Jurnal Pendidikan dan Teknologi Indonesia Vol 4 No 11 (2024): JPTI - November 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.481

Abstract

Penelitian ini berfokus pada pengembangan model Convolutional Neural Network (CNN) untuk mendiagnosa jenis katarak mata secara otomatis dan akurat. Data yang digunakan dalam penelitian ini dibagi menjadi dua bagian, yaitu 80% untuk pelatihan dan 20% untuk validasi. Model CNN dilatih menggunakan teknik augmentasi data untuk meningkatkan kinerja dan generalisasi. Setelah melalui proses pelatihan yang intensif, model menunjukkan kinerja yang sangat baik, dengan akurasi mencapai 98% pada data pelatihan dan 99% pada data validasi. Selain itu, hasil evaluasi menunjukkan bahwa model memiliki precision, recall, dan F1-score yang sangat tinggi. Untuk kelas mature dilabeli numerik 0 dengan precision adalah 0.99, recall 0.99 dan F1-score 1.00. Untuk kelas immature dilabeli numerik 1 dengan precision adalah 1.00, recall 0.99 dan F1-score 0.99. Hasil ini menegaskan bahwa model memiliki kemampuan kuat dalam mendiagnosa jenis penyakit mata karatak. Keberhasilan model menunjukkan potensinya untuk digunakan sebagai alat bantu diagnosis katarak dalam praktik klinis, memungkinkan deteksi yang lebih cepat dan akurat. Model juga dapat membantu mengurangi beban kerja dokter mata dengan menyediakan diagnosis awal yang dapat diandalkan. Namun, untuk memastikan keandalan dan generalisasi model dalam berbagai situasi klinis, diperlukan uji coba tambahan dengan dataset yang lebih besar dan beragam. Penelitian memberikan landasan penting untuk pengembangan lebih lanjut dalam sistem diagnostik berbasis kecerdasan buatan (AI) yang dapat meningkatkan kualitas perawatan mata dan mempercepat proses diagnosa.
Implementasi Algoritma K-Means Menggunakan RapidMiner untuk Klasterisasi Data Obat Pada Rumah Sakit Royal Prima Siregar, Afrahul Hidayah; Sihotang, Dohardo Dulisep; Wijaya, Bayu Angga; Siregar, Saut Dohot
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 7 No. 2 (2024): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v7i2.5537

Abstract

Efficient management of drug data is a crucial element in hospital operations to ensure the availability and proper use of medications. This research aims to implement the K-Means clustering algorithm using RapidMiner software to cluster drug data at RS Royal Prima. The dataset includes information such as drug type, category, price, and intake frequency. The clustering process begins with data preprocessing stages, such as cleaning and normalization. The optimal number of clusters is determined using the elbow method and silhouette analysis. The clustering results show that drug data can be grouped into several large clusters based on specific characteristics. This analysis helps identify patterns of drug use that can support clinical decision-making and improve inventory management. This implementation demonstrates that using RapidMiner to cluster pharmaceutical data is effective and provides valuable insights to enhance hospital operations.
Google Translate Mengubah Bahasa Batak Menjadi Bahasa Indonesia Calvin Calvin; Piter Antonius; Saut Dohot Siregar
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 2 (2025): April: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v3i2.399

Abstract

Google Translate is an artificial intelligence-based translation service developed by Google. Since its introduction in 2006, this service has continued to create with the application of Neural Machine Translation (NMT) technology, which improves the accuracy and fluency of translation compared to previous methods. This study aims to determine the effectiveness and limitations of Google Translate in translating Batak into Indonesian. The research method used is descriptive qualitative with a comparative approach of Google Translate translation results and manual translations by professional translators. The study results show that Google Translate can translate basic words and simple sentences quite well. However, there are several limitations, such as a lack of understanding of the cultural context, idioms, and dialect variations in the Batak language. In addition, the translation is also influenced by the limitations of the database and vocabulary enrichment in this service. Thus, although Google Translate can be a tool in translation, users still need to do manual verification to ensure accuracy, especially in fields that require high precision such as law, academics, and professional communication.
Implementasi Internet Of Things Dalam Transformasi Digital Kesehatan (Literature Review) Seftia Angelina; Saut Dohot Siregar
AT-TAKLIM: Jurnal Pendidikan Multidisiplin Vol. 2 No. 3 (2025): At-Taklim: Jurnal Pendidikan Multidisiplin (Edisi Maret)
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/at-taklim.v2i3.197

Abstract

The Internet of Things (IoT) has emerged as one of the key innovations in modern healthcare. By connecting medical devices, IoT provides real-time access to patient data, enhances service efficiency, and supports faster clinical decision-making. This literature review evaluates the application of IoT in healthcare, covering its benefits in remote monitoring, the development of smart devices, and the challenges faced regarding data security and infrastructure. This study employs a qualitative descriptive approach with quality assessment parameters to evaluate relevant literature. The findings reveal that IoT plays a significant role in creating more accessible healthcare services, although further development is needed to address existing technological limitations.
Analisis Wawasan Penjualan Supermarket dengan Data Science Harahap, Mawaddah; Rozi, Fachrul; Yennimar, Yennimar; Siregar, Saut Dohot
Data Sciences Indonesia (DSI) Vol. 1 No. 1 (2021): Article Research Volume 1 Issue 1, June 2021
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v1i1.1173

Abstract

Data science atau ilmu data adalah suatu disiplin ilmu yang khusus mempelajari data, khususnya data kuantitatif (data numerik), baik yang terstruktur maupun tidak terstruktur. Pemanfaatkan siklus dalam pengembangan analisis untuk membuat keputusan bisnis yang praktis dan berbasis data, dan menerapkan perubahan berdasarkan keputusan tersebut. Makalah ini menyajikan analisis wawasan yang berguna pada kumpulan transaksi penjualan supermarket selama 3 bulan dari 3 cabang yang berbeda. Berdasarkan hasil analisis nilai rating terting adalah 10, terendah 4 dengan rata-rata rating produk 6.9 dan wanita lebih dominan membeli produk Aksesoris Fashion dan pria Kesehatan & Kecantikan
Rancang Bangun IoT Otomatis Berbasis Sensor PIR untuk Menghemat Energi Listrik pada saat Ruangan Kosong Okman Tampubolon, Juan Renhard; Ramadan, Wahyu; Buulolo, Julianus; Purba, Piona Pricilia; Siregar, Saut Dohot
Jurnal Sistem Komputer dan Informatika (JSON) Vol 6, No 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8805

Abstract

Pemakaian listrik yang tidak efisien, terutama dari lampu yang menyala di ruangan kosong, menyumbang sekitar 30% pemborosan energi di sektor rumah tangga, perkantoran, dan industri menurut Kementerian Energi dan Sumber Daya Mineral Indonesia. Penelitian ini merancang sistem otomatisasi lampu berbasis Internet of Things (IoT) menggunakan sensor PIR (Passive Infrared) dan mikrokontroler Arduino untuk mendeteksi keberadaan manusia. Sistem ini akan mengaktifkan atau mematikan lampu secara otomatis berdasarkan aktivitas di dalam ruangan, sehingga mampu menghemat konsumsi energi listrik. Studi dilakukan di ruang kelas dan laboratorium Universitas Prima Indonesia dengan menganalisis pola penggunaan energi. Hasil pengujian menunjukkan bahwa sistem mampu merespons gerakan dalam ±2 detik, mengurangi konsumsi energi hingga 30–50% tergantung kondisi ruangan. Penelitian ini juga mengisi celah dari studi sebelumnya yang belum mengintegrasikan pemantauan real-time dan fitur override manual. Dengan penggunaan sensor PIR yang lebih akurat dibandingkan sensor cahaya (LDR), sistem ini mendukung efisiensi energi yang lebih adaptif dan berkelanjutan. Rancang bangun ini berpotensi diterapkan luas dalam lingkungan akademik maupun industri.
Implementasi Algoritma Logistic Regression dalam Memprediksi Penyakit Jantung Aritonang, Madhani Gokma Hot; Parangin angin, Reynaldi Valentino; Tambunan, Raymond Hosea; Simatupang, Ronauli; Siregar, Saut Dohot
Dinamik Vol 30 No 2 (2025)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v30i2.10306

Abstract

Penyakit jantung merupakan salah satu penyakit dengan angka kematian tertinggi di negara maju bahkan dunia. Penyakit jantung dapat mengancam jiwa jika tidak ditangani dengan serius. Jumlah penderita penyakit jantung meningkat setiap tahunnya. Penyakit jantung dapat disebabkan oleh beberapa faktor, yang utama adalah konsumsi alkohol berlebihan, kebiasaan merokok, dan faktor keturunan. Penelitian ini bertujuan untuk memprediksi dan mencegah penyakit jantung sejak dini menggunakan algoritma pembelajaran mesin, yaitu regresi logistik. Data yang digunakan untuk pelatihan dan pengujian algoritma regresi logistik sebanyak 1.190 data, yang terbagi menjadi 80% data pelatihan dan 20% data pengujian. Hasil pengujian menunjukkan bahwa model dapat memprediksi dengan akurasi sebesar 86%. Setelah model dibuat, model tersebut diimplementasikan ke dalam situs web. Penelitian ini diharapkan dapat berkontribusi pada diagnostik yang dapat membantu deteksi dini penyakit jantung.
Analisis Strategi Penjualan Produk Digital di Shopee: Studi Observasi dan Eksperimen pada Toko Pribadi: Penelitian Milton Siahaan; Saut Dohot Siregar
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 1 (Juli 2025 -
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i1.2528

Abstract

Indonesian e-commerce has grown rapidly over the past five years, particularly for digital products due to easy access and flexibility. This study analyzes digital product sales strategies on Shopee through observations of ten stores (March–July 2025) and experiments at the researcher's store. The main focus includes pricing strategies, platform promotions, visual optimization, upload times, and reputation management through reviews and customer interactions. Results show that initial competitive pricing strategies, attractive visuals, and content consistency increase visibility and sales. A store's reputation with positive reviews significantly impacts consumer trust and sales sustainability. Experiments demonstrate that penetration pricing effectively accelerates positive reviews and initial loyalty, resulting in stable sales even when prices are raised. Research barriers include limited access to competitor data and research time management. The study recommends a reputation-based strategy and competitive initial pricing as the foundation for the long-term success of digital products in e-commerce.
Co-Authors Achmad Ridwan, Achmad Agung Prabowo Andreas Saputra Nainggolan Anthony Hutapea Aritonang, Madhani Gokma Hot Bayu Angga Wijaya Berkat Panjaitan Buulolo, Julianus Calvin Calvin Chandra, Willyam Dapit Parasian Simarmata Debora Indriama Delima Sitanggang, Delima Denis Kinarwan Dessy Permatasari Ermi Girsang Fachrul Rozi Felison, Thomas Firtan, Erwin Conery Gari, Paskah Kurniawan Ginting, Riski Titian Hari Salim Butar-Butar Hendra Mangaraja Tua Apul Tampubolon Hotromasari Dabukke HS, Christnatalis Irfan Ernala Khavitanjali, Khavitanjali Lestari Lestari Loi, Anita Manday, Dhanny Rukmana Mawaddah Harahap Mawaddah Harahap Mawaddah Harahap, Mawaddah Milton Siahaan Nita Sintami Nurul Khairina Nurul Khairina Okman Tampubolon, Juan Renhard Panjaitan, Ruth N Parangin angin, Reynaldi Valentino Perangin Angin, Despaleri Piter Antonius Polfan, Wilsen Pratama , Jordan Putra Purba, Piona Pricilia Ramadan, Wahyu Ricky Supriyanto Ricky Wijaya Riki Marthin Simanjuntak Robin Robin Rozi, Fachrul Seftia Angelina Sihotang, Dohardo Dulisep Silvin Berkat Harapan Mendrofa Simangunsong, Andri Daniel Martua Simarmata, Allwin M Simatupang, Ronauli Siregar, Afrahul Hidayah Steven Steven Tambunan, Raymond Hosea Togar Timoteus Gultom Yawin, Helbert Yennimar Yennimar Yennimar, Yennimar Yohana Marbun Yordan, Vedi Yusra Uli Rasita Ginting