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All Journal Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Sains dan Teknologi Jurnal Tekinkom (Teknik Informasi dan Komputer) INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) JURNAL MITRA PRIMA Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam International Journal of Information System & Innovative Technology TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer International Journal of Information System and Innovative Technology INOVTEK Polbeng - Seri Informatika
Delima Sitanggang, Delima
Universitas Prima Indonesia
Aerospace Engineering Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Veterinary

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SISTEM BERBASIS ANDROID UNTUK RESERVASI TIKET BUS Simanjuntak, Mega Herlin; Sitanggang, Delima; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 4 No. 2 (2021): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v4i2.1588

Abstract

Mengikuti perkembangan teknologi dibidang mobile device yang diterapkan pada satu aplikasi yang dapat mencakup berbagai fitur pemesanan seperti pemesanan kamar hotel, pemesanan tiket pesawat, pemesanan tiket kereta api, pemesanan tiket bus, dan juga pemesanan tiket wisata sangat efisien untuk proses pencarian lokasi serta tujuan. Akan tetapi, aplikasi seperti ini membatasi interaksi langsung antara user dengan Perusahaan yang dituju dikarenakan adanya pihak aplikasi yang berperan sebagai penghubung serta memiliki kapasitas yang cukup besar untuk di download. Sehingga pada pemesanan tiket di aplikasi tersebut tidak kondusif dikarenakan ketika user telah booking tiket yang sudah dipilih dapat dibatalkan oleh pihak aplikasi tanpa menginformasikan kepada user terlebih dahulu.
Penerapan Pemantauan Proses hukum Pidana berbasis Android Marpaung, Aldo Andy Yoseph Tama; Tampubolon, Irfan Saputra; Situkkir, Miando Mangara; Simarmarta, Brando Benedictus; Sitanggang, Delima; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 5 No. 1 (2021): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v5i1.1844

Abstract

Tujuan utama hukum adalah untuk mewujudkan ketertiban. Dalam melakukan tugas penanganan narapidana yang terkena sanksi hukum akan sangat berlangsur lama dalam melaksanakan pidana. Maka dari itu, Tujuan dari penelitian ini adalah Membuat sebuah Aplikasi yang dimana dapat mempermudah mengetahui tentang narapidana yang terkena hukuman.
Model Prediksi Obesitas dengan Menggunakan Support Vector Machine Sitanggang, Delima; Sherly, Sherly
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 5 No. 2 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v5i2.2443

Abstract

Obesitas atau kelebihan berat badan merupakan kondisi dimana adanya abnormalitas maupun lemak berlebih pada individu yang berperan sebagai salah satu faktor penyakit yang mengancam kesehatan seseorang. Menurut WHO, data dari tahun 1975 hingga 2016 tingkat obesitas pada anak dan remaja dengan umur 5 sampai 19 tahun terus meningkat hingga lebih dari empat kali lipat dari 4% menjadi 18%. Pada masa sekarang, obesitas tidak hanya menjadi masalah pada negara yang memiliki pendapatan perkapita tinggi, negara berkembang dengan pendapatan perkapita rendah menengah juga mengalami peningkatan jumlah obesitas dengan tingkat peningkatan 30% lebih tinggi dari negara maju. Pada penelitian ini, berfokus pada memprediksi tingkat persentase lemak pada badan menggunakan Support Vector Machine. Data target yang akan diprediksi adalah ‘BodyFat’ dengan mengacuhkan ‘Density’ karena pengukuran persentase ‘BodyFat’ diambil dari nilai ‘Density’. Model prediksi ini dibangun untuk mempermudah proses penemuan tingkat densitas dari tubuh manusia dikarenakan prosesnya pengambilan datanya yang tidak mudah.Tujuan dari penelitian ini adalah untuk menilai kemampuan Support Vector Machine dalam melakukan regresi serta mempersiapkan algoritma prediksi bertipe regresi dengan nilai peforma yang baik. Manfaat dari penelitian ini adalahuntuk memperoleh model prediksi data yang dapat membantu memprediksi nilai persentase lemak pada badan sehingga dapat digunakan untuk kelengkapan data serta penyajian informasi tanpa perlu memperhatikan faktor bentuk badan yang beragam. Proses implementasi algoritma SVR dapat dengan baik melakukan regresi dengan tingkat akurasi akhir 71.80% dan MSE 17.76. Sistem prediksi yang dihasilkan dengan algoritma mampu membantu dalam penentuan otomatis persentase lemak pada badan tanpa perlu pengukuran densitas badan yang memerlukan pengukuran dalam air dikarenakan volume tubuh manusia yang beragam dan bervariasi.Persentase lemak pada badan merupakan informasi yang penting baik untuk keperluan diagnosa maupun sebagai informasi peringatan yang dikarenakan apabila persentase berlebih dapat menyebabkan penyakit beresiko tinggi seperti type-2 diabetes dan penyakit jantung lainnya.
SHORT-TERM FORECAST FOR THE GROWTH OF INDONESIA'S NEW RENEWABLE ENERGY USING THE ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM Ginting, Riski Titian; Tri Nugraha, Yoga; Perangin-Angin, Despaleri; Gultom, Togar Timoteus; Nainggolan, Winner Parluhutan; Sitanggang, Delima
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 2 (2023): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3477

Abstract

Electric power plants always use fossil fuels such as coal, oil, etc. However, the fossil fuel supply in Indonesia is decreasing from year to year. This causes power plants to be powered by using fuels that will not run out, such as solar Energy, water, wind, and others. Solar Energy, water, wind, and others are Alternative Energy or can also be called New and Renewable Energy. To guarantee a power plant powered by alternative Energy, it must be analyzed regarding the growth of new and renewable Energy. The method used in analyzing the development of new and renewable Energy is the Adaptive Neuro Fuzzy Inference System Method. MW or 0.599%. This result has increased yearly in Indonesia's new and renewable energy growth.
APPLICATION OF KNN METHOD FOR CLASSIFICATION OF ARRHYTHMIA TYPES BASED ON ECG DATA Manao, Sonatafati; Sitanggang, Delima; Sagala, Albert; Oktarino, Ade; Turnip, Mardi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6010

Abstract

World Health Organization (WHO) data from June 2024 shows that 31% of adults worldwide or 1.8 billion people do not do physical activity. With that, adults are at higher risk of developing cardiovascular disease and causing an economic and social burden on people with heart disease. K-Nearest Neighbor (KNN) is a machine learning method that can be used to classify or predict heart disease conditions. KNN works by finding the closest data point in the training dataset and then using the class labels of those neighbors to classify new data points. In the context of heart disease, this can be used to predict the likelihood of someone having heart disease. Recording the electrical activity of the heart using a 3-led ECG to determine heart health as well as being material for classification. Exploring the use in the diagnosis of heart disease by focusing on screening and classification of heart disease. By utilizing the KNN method, it has the potential to produce a model that can assist in clinical decision making. Improving the prevention of heart disease and accelerating diagnosis through more sophisticated and technology-based analysis of patient health data.
Classification Of Hypertension Using K-Nearest Neighbor Based On Photoplethysmograph Data And Blood Pressure Estimator Sinaga, Jasmin William Natanael; Tampubolon, Tasya Rouli Christy; Simanjuntak, Ester Farida; Sitanggang, Delima; Rizal, Reyhan Achmad
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/zswzf122

Abstract

Hypertension is a persistent cardiovascular condition, often termed the “silent killer” because it typically presents no symptoms in its early stages. To address the shortcomings of traditional blood pressure monitoring methods, this study develops a classification system that leverages photoplethysmography (PPG) signals in combination with the K-Nearest Neighbor (KNN) algorithm. PPG provides a promising non-invasive solution that is readily adaptable to portable devices. The classification process employs the Euclidean Distance method to determine the similarity between new data samples and previously labeled instances. Data were collected from 276 individuals spanning various age groups using PPG sensors connected to the MR-IAT Robot Covid platform. The system categorizes individuals into normotensive, prehypertensive, stage 1, and stage 2 hypertension groups. The study evaluates the performance of the KNN algorithm based on its ability to predict blood pressure categories from morphological features extracted from the PPG signals. Ultimately, the outcomes of this research are expected to advance the development of efficient, real-time, continuous blood pressure monitoring systems through user-friendly machine learning approaches.
Application of Data Mining for Tuberculosis Disease Classification Using K-Nearest Neighbor Sitanggang, Delima; Simangunsong, lamria; Sundah, Geertruida Frederika; Hutahaean, Rani; Indren, Indren
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2218

Abstract

This study aims to find out how much the application of the K-NN method and the accuracy value obtained by the K-NN method in clarifying data of Tuberculosis patients. This research focuses on improving public health and developing science to help people prevent and overcome tuberculosis. This type of research is quantitative. The literature study used is the documentation study. The method used by the K-Nearest Neighbor Algorithm. The results of the study showed that the process of applying data mining for the classification of tuberculosis disease using the K-Nearest Neighbor method obtained a final result of 80% accuracy. Thus, it can be concluded that the K-Nearest Neighbor algorithm is good.
Application of Support Vector Machine in Measuring Stress Levels Based on EEG Signals Wijaya, Bryan; Sitanggang, Delima; Lee, Brandon; Angie, Vicky; Siahaan, Eric Simon Giovanni
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): 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.v8i1.6584

Abstract

This study aims to classify stress levels based on electroencephalography (EEG) signals using the Support Vector Machine (SVM) algorithm. The data used in this study came from 21 subjects with a total of 379 datasets, which included the main variables of Subject, Electrode Channel (E), Theta, Beta 1, and Beta 2. Preprocessing was done to ensure data quality, including blank data elimination, normalization, and feature engineering. One of the main features developed was the Beta Average, which was obtained by calculating the average between Beta 1 and Beta 2, and stress level classification, which was determined based on the comparison between the Beta Average and Theta. The SVM algorithm was applied to build a stress classification model with an initial stage of manual calculation to understand the basic concepts, followed by the Python programming language implementation. The evaluation results show that the developed model has an accuracy of 92.76%, with the highest precision, recall, and f1-score values reaching 100% and the lowest value of 85%. The confusion matrix analysis showed that the model could classify low stress with 100% accuracy, while it reached 87.8% for high stress. The findings of this study prove that the SVM algorithm effectively classifies EEG signal-based stress levels. This model can be the basis for further development of stress detection methods, especially in mental health and neuroinformatics applications.
Implementation of Grid Search Optimization Algorithm and Adaptive Response Rate Exponential Smoothing for Hyperparameter Tuning in Production Activity Determination Sanjaya, Federico; Alvina, Jesslyn; Putra, Muhammad Amsar; Sitanggang, Delima
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): 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.v8i1.6593

Abstract

This research aims to improve the accuracy of production planning at PT Bilah Baja Makmur Abadi by combining the Adaptive Response Rate Exponential Smoothing (ARRES) algorithm and Grid Search optimization. The main problems faced are unpredictable demand fluctuations, dead stock risks, and high operational costs due to imbalances between production and demand. The ARRES algorithm is used for demand forecasting with adaptive exponential weighting, while Grid Search optimizes the alpha and initial year parameters to improve prediction accuracy. This study uses a 5-year sales dataset (2017-2021) with model evaluation using Mean Absolute Percentage Error (MAPE). The results showed that the combination of Grid Search and ARRES optimization algorithms proved effective in helping predict production needs. This can be seen from the significant decrease in the average MAPE value, which is 7.07% using this combination method, compared to 8.18% in the ARRSES method. The lower MAPE value indicates that the Grid Search method is effective in optimizing the ARRSES model parameters. With relatively high prediction accuracy (MAPE < 10%), this method is able to cope with unexpected demand fluctuations.
Comparison of LightGBM With XGBoost Algorithms in Determining Arrhythmia Classification in Students Sitanggang, Delima; Wilbert Solo, Eddrick; Immanuel Sinaga, Ferdy; Jorgi L.Tobing, Stefanus; Hutasoit, Feliks Daniel; Prabowo, Agung
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5015

Abstract

Arrhythmia is a heart rhythm disorder that may occur unpredictably with life-threatening risk if it were not treated immediately. This heart disorder generally affects the elderly, but symptoms of this disorder can also arise in children and adolescents, especially for those with heart problems or are often under stress. The implementation of this research is aimed at analyzing the symptoms of early arrhythmia in adolescent children using electrocardiogram signals. In order to obtain the best possible results in determining the higher performing algorithm, two machine learning methods were used to predict the classification of arrhythmia which will be compared for their accuracy. The subjects of this study included 106 students from SMK Swasta Teladan Sumatera Utara 2 located in the city of Medan, of which 72 final subject data were used to train the capability of both models used to predict arrhythmia classification categorized into four categories, namely normal, abnormal, potential of arrhythmia, and high potential of arrhythmia. The LightGBM model outperformed the XGBoost model, with 95.11% accuracy and 95.03% F1 Score, and although the loss value of the LightGBM model is higher than the loss value of the XGBoost model, the difference between these two values is negligible and the loss value of LightGBM can be considered as excellent with a value of 0.1503. This research contributes to the advancement of digital health by demonstrating the potential of machine learning-based ECG analysis for highly accurate early arrhythmia detection in adolescent, non-clinical populations.
Co-Authors -, Amalia ., Calvin ., Efendy ., Kelvin Abdi Dharma Achmad Ridwan, Achmad Ade Sahputra Nababan Agung Prabowo Agustinus Lumban Raja Albert Sagala, Albert Alvina, Jesslyn Ambarita, Rivandu Amir Mahmud Husein, Mawaddah Harahap, Amir Angie, Vicky Anita Anita Anita Christine Sembiring Ayu Rahayu Sagala Ayu Rosalya Sagala Barus, Ertina Sabarita Bolon, Debby Novriyanti Br Tp. Butarbutar, Serly Yunarti Cloudia Stevani Saragih Sumbayak Cristian Andika Tarigan Dahlian, Ryo Benhard David David Debby Novriyanti Br Tp.Bolon Djuli, Zachary Esther Mayorita Nababan Etriska Prananta S. Evta Indra Evta Indra Faijriah Nazla Sahira Felix Felix Ginting, Arico Sempana Ginting, Nessa Sanjaya Ginting, Riski Titian Grace Aloina Greace HS, Christnatalis Hutahaean, Rani Hutasoit, Feliks Daniel Iboy Erwin Saragih, Rijois Immanuel Sinaga, Ferdy Indra, Evta Indren, Indren Intan Susanti Simarmata Jefri Syah Putra Laoli Jorgi L.Tobing, Stefanus Juan Juanta, Palma Kumar, Sharen Lee, Brandon Lidya Silalahi Lumbantoruan, Nurima Manao, Sonatafati Manday, Dhanny Rukmana Mardi Turnip, Mardi Maria Yostin Br Tarigan Marlince N.K Nababan Marpaung, Aldo Andy Yoseph Tama Marpaung, Cantika Matthew Oullanley Lee Meri Natasia Napitupulu Mita Aprila Silpa Simanjuntak Muhammand Ridho Muliadi Marianus Sirait Musa Andrew Loyd Sitanggang Nababan, Marlince N.K Nainggolan, Winner Parluhutan Nanchy Adeliana Br S. Muham Napitupuluh, Christian Deniro Niken Sihombing Nina Purnasari Nova Riani Fransiska Novanius Lahagu Oktarino, Ade Oktoberto Perangin-angin Pamungkas, William Aldo Perangin Angin, Despaleri Perangin-angin, Despaleri Pungki Laurensius Ritonga Putra, Muhammad Amsar Rijois I. E. Saragih Rizal, Reyhan Achmad Sadarman Zebua Saljuna Hayu Rangkuti Sanjaya, Federico Saragi, Yosua Morales Saragih, Rini Hartati Sarah Simangunsong Saut Parsaoran Tamba sherly sherly Siahaan, Edivan Wasington Siahaan, Eric Simon Giovanni Sihotang, Putri Anasia Simangunsong, lamria Simanjuntak, Ester Farida Simanjuntak, Mega Herlin Simanjuntak, Ruth Marsaulina Simarmarta, Brando Benedictus Sinaga, Jasmin William Natanael Sion Putri Zalukhu Siregar, Saut Dohot Sitanggang, Maria Natalenta Siti Aisyah Siti Aisyah Sitompul, Chris Samuel Sitorus, Angelina Monica Situkkir, Miando Mangara Solly Aryza Sri Wahyu Tarigan Sri Wahyuni Tarigan Sumita Wardani Sundah, Geertruida Frederika Suyanto, Jao Han Tampubolon, Irfan Saputra Tampubolon, Johanes Joys Ronaldo Tampubolon, Tasya Rouli Christy Tarigan, Julio Putra Tarigan, Nina Veronika Tarigan, Sri Wahyuni Tifanny, Tifanny Togar Timoteus Gultom Wijaya, Bryan Wilbert Solo, Eddrick Winarti Pasaribu Yennimar Yennimar, Yennimar Yoga Tri Nugraha Yonata Laia Yumna, Farhan