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All Journal Dinamik Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) 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
Siregar, Saut Dohot
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mechanical Engineering Physics Social Sciences

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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.
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.
Pelatihan Internet Of Things (IoT) Untuk Meningkatkan Kompetensi Digital Siswa Di Smk Negeri Jorlang Hataran Perangin Angin, Despaleri; Gultom, Togar Timoteus; Sitanggang, Delima; Yennimar, Yennimar; Prabowo, Agung; Siregar, Saut Dohot; Ridwan, Achmad; Ginting, Riski Titian; HS, Christnatalis; Manday, Dhanny Rukmana
Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Vol. 7 No. 1 (2025): Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Publisher : Politeknik Negeri Batam

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

Abstract

The purpose of this community service activity is to enhance digital competency skills at SMK Negeri I Jorlang Hataran. The method used in the implementation of this activity is training through the delivery of materials, practical training on the assembly and programming of IoT devices, and a question-and-answer session. The participants of this activity consist of 37 students from the 11th grade RPL (Software Engineering) major. The instruments used in this activity include participant feedback and activity documentation. The results of the implementation show that the participants' responses to the basic computer training were overall in the good category. The percentage of student responses reached 98.20%, which falls into the very good category.
Comparison of K Nearest Neighbor Algorithm with Apriori Algorithm to Analyze Lifestyle Patterns in Hypertensive Patients Steven, Steven; Wijaya, Ricky; Yawin, Helbert; Siregar, Saut Dohot
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 6 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i6.1134

Abstract

Hypertension is one of the most influential cardiovascular diseases that can lead to organ disorders such as heart dysfunction or stroke and hypertension is often discovered by chance. This disease can interfere with the work of other organs if left untreated, especially the heart and kidneys. Not paying attention to diet, exercise, stress, smoking, and drinking alcohol can all be causes of increased risk of hypertension. To predict people with hypertension and find out the comparison of behavior and lifestyle patterns with hypertension patients using a priori algorithm in the case study of Sei Semayang Health Center. So the results of rapidminer use the apriori algorithm to analyze the Comparison of K the nearest Neighbor Algorithm with the apriori Algorithm to Analyze Lifestyle Patterns in Hypertensive Patients the results obtained is U1 which means there are people with hypertension aged 25-38 years who have more hypertension and the results are H2 which means that people have but do not control to the doctor with a pattern style such as consuming alcohol, Smoking, and lack of exercise, sugar consumption, consumption of saturated fat and foods that contain a lot of salt and rarely consume vegetables or fruits and foods containing MSG then more and more people who have hypertension with an unhealthy lifestyle.
Implementation of the YOLO Method for Detection of Human Emotions Based on Facial Mimics Felison, Thomas; firtan, Erwin conery; Steven, Steven; Chandra, Willyam; Siregar, Saut Dohot
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 10 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i10.1196

Abstract

Emotion detection through facial expression recognition plays an important role in everyday life, such as how to respond correctly to emotional expressions in social interactions, so that you can establish and build verbal or nonverbal communication with other people and so on. Facial expressions are facial changes in response to a person's emotional state, intentions, or social communication. Face detection is the first step that must be taken in facial analysis, including facial expression recognition. There are many methods that can be used to carry out the face detection process, such as the YOLO method. This YOLO method reframes object detection as a single regression problem, directly from image pixels to bounding box coordinates and class probabilities. By using the YOLO method, the process only needs to look once at the input image, to predict what objects are in the image and where those objects are. Based on the results of the tests carried out, the YOLO method can be used to detect human facial expressions with a success rate of 80%, with neutral, surprise and disgust facial expressions having a good level of accuracy and fear facial expressions having a poor accuracy level. The YOLO method is able to detect facial expressions of humans who wear accessories such as glasses.
Co-Authors Achmad Ridwan, Achmad Agung Prabowo Aritonang, Madhani Gokma Hot Bayu Angga Wijaya Buulolo, Julianus Chandra, Willyam Delima Sitanggang, Delima Felison, Thomas Firtan, Erwin Conery Gari, Paskah Kurniawan Ginting, Riski Titian HS, Christnatalis Khavitanjali, Khavitanjali Loi, Anita Manday, Dhanny Rukmana Mawaddah Harahap, Mawaddah Okman Tampubolon, Juan Renhard Panjaitan, Ruth N Parangin angin, Reynaldi Valentino Perangin Angin, Despaleri Polfan, Wilsen Pratama , Jordan Putra Purba, Piona Pricilia Ramadan, Wahyu Ricky Wijaya Rozi, Fachrul Sihotang, Dohardo Dulisep Simangunsong, Andri Daniel Martua Simarmata, Allwin M Simatupang, Ronauli Siregar, Afrahul Hidayah Steven Steven Tambunan, Raymond Hosea Togar Timoteus Gultom Yawin, Helbert Yennimar, Yennimar Yordan, Vedi