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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 Sistem Komputer dan Informatika (JSON) 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 Journal of Deep Learning, Computer Vision and Digital Image Processing 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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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 | DOI: 10.30871/abdimaspolibatam.v7i1.10114

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.
EEG Signal Classification using K-Nearest Neighbor Method to Measure Impulsivity Level Ginting, Arico Sempana; Simanjuntak, Ruth Marsaulina; Lumbantoruan, Nurima; Sitanggang, Delima
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2154

Abstract

Impulsivity is the tendency to act without considering consequences or without careful planning. It involves a quick response to a stimulus without sufficient consideration of the consequences. Impulsivity needs to be measured and detected because it has a significant impact on various aspects of a person's life. The factors that influence the level of impulsivity include social environment, stress level, mental health, and genetic factors. Impulsivity can be divided into multiple components, such as reduced sensitivity to unfavorable behavioral outcomes, a disregard for long-term implications, and quick and spontaneous responses to stimuli. Electroencephalogram (EEG) studies can identify specific brain wave patterns such as, Alpha, Betha, Theta, and Gamma waves everything based on an individual brain's level of impulsivity. Signals from the brain are processed to extract specific features that reflect the user's intentions. EEG records brain activity without surgery, and this information is used for the diagnosis, monitoring, and treatment of neurological diseases, as well as scientific research on the brain and mind. K-Nearest Neighbor (KNN) is a classification algorithm that functions by utilizing several K nearest data values (its neighbors) as a reference to determine the class of new data. The K-Nearest Neighbors (KNN) algorithm is used for classification, clustering, and pattern recognition in EEG data where clustering is in 4 classifications (Impulsive, Not Impulsive, Potentially Impulsive, and Very Potentially Impulsive). This classification model shows high accuracy (Training Data: 94.7%, Testing: 91.3%, and Validation Data: 91.8%). This research shows that the KNN algorithm is effective for assessing the degree of impulsivity.
COMPARING REGRESSION METHODS FOR ASSESSING AND PREDICTION THE IMPACT OF SALARY INCREASES ON EMPLOYEE PERFOMANCE Palma Juanta; Zachary Djuli; Tifanny Tifanny; Delima Sitanggang; Anita Anita
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10098

Abstract

In today’s competitive digital era, data-driven decision-making is key to enhancing the efficiency of human resource management. One of the main challenges is objectively assessing the impact of salary increases on employee performance, which is often assumed to be a primary motivator but rarely proven quantitatively. This study conducts a comparative analysis of two data mining methods, Linear Regression and Decision Tree Regression, to assessing and predicting the impact of salary increases on employee performance. A case study was conducted at PT. Taipan Agro Mulia using the company’s internal historical data. The analysis shows that Linear Regression performed better with an R-Square value of 0.731 or 73.1%, indicating that 73.1% of the variation in employee performance can be explained by salary increases. In comparison, Decision Tree Regression achieved an R-Square value of 0.700 or 70.0%. Additionally, Linear Regression recorded lower prediction errors (MAE = 4.78; MSE = 38.60; RMSE = 6.21) than Decision Tree (MAE = 5.61; MSE = 66.41; RMSE = 8.15). These findings demonstrate that data analysis approaches can serve as a strong foundation for formulating strategic salary policies aimed at improving employee performance
Pengembangan Sistem Informasi Manajemen Penjualan Aksesoris Motor Berbasis Web Julio Putra Tarigan; Abdi Dharma; Siti Aisyah; Delima Sitanggang; Yosua Morales Saragi; Mardi Turnip
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp216-219

Abstract

In this era of globalization, computerized systems have been used by many parties, both agencies, organizations and educational institutions. A computerized system is an information system that will design transaction data into useful information and aims to help make efficient decisions. However, at this time, PT. Surya Mandiri Motor does not yet use a computerized system, so errors often occur in recording, calculating transaction data, there are difficulties in searching for data and difficulties in making reports. This sales information system can be a solution that can simplify data processing so that the sales transaction process will be faster, more precise and accurate. This system was built using the PHP programming language and MySQL database.
Klasifikasi Komentar Promosi Judi Online pada YouTube Menggunakan TF-IDF dan Support Vector Machine Rinaldi Fauzan; Delima Sitanggang; Reyhan Achmad Rizal
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9769

Abstract

Penelitian ini bertujuan mengklasifikasikan komentar promosi judi online pada platform YouTube menggunakan metode Support Vector Machine (SVM). Data penelitian berasal dari dataset publik Kaggle yang berisi komentar pengguna YouTube terkait promosi judi online. Tahapan penelitian meliputi preprocessing teks, ekstraksi fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF), serta klasifikasi menggunakan SVM kernel linear. Hasil pengujian menunjukkan bahwa model SVM memperoleh akurasi sebesar 95,94% dengan nilai precision, recall, dan F1-score yang tinggi dan seimbang. Hasil analisis juga menunjukkan bahwa mayoritas tanggapan pengguna cenderung berada pada kelas non-promosi terhadap konten judi online. Selain itu, SVM menunjukkan performa yang lebih baik dibandingkan Naïve Bayes pada dataset yang digunakan. Temuan ini menunjukkan bahwa kombinasi TF-IDF dan SVM efektif digunakan untuk mengidentifikasi pola tanggapan pengguna terhadap konten judi online di YouTube
Attention Span Classification of Social Media Users Using Multi-Kernel Support Vector Machine Based on Survey Data Reza Pahlevi; Darren Lucius; Diasta Natanael Sembiring; Delima Sitanggang
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1193

Abstract

Purpose – This study aims to examine whether self-reported attention-related difficulty categories among social media users can be classified using a leakage-free machine learning framework. It addresses the risk of inflated performance in survey-based classification by excluding the same items used to construct the target label from the predictor set.Methods – The study used the public Social Media and Mental Health (SMMH) Kaggle dataset with 478 valid respondents. A three-class label was constructed from Q10, Q12, and Q14 using percentile thresholds (P33 = 9.0; P66 = 12.0), producing High (n = 208), Medium (n = 152), and Low (n = 118) categories. These label-generating items were excluded from predictors. The remaining variables were processed in a scikit-learn Pipeline using MinMax scaling, ordinal encoding, and One-Hot Encoding. Multi-kernel SVM models and five baseline classifiers were evaluated using a stratified 70:30 split, cross-validation, F1 metrics, balanced accuracy, and permutation importance.Findings – Random Forest achieved the highest performance, with 63.19% accuracy and 62.26% weighted F1. Linear SVM was the best SVM model, achieving 61.81% accuracy, 60.08% weighted F1, 58.99% macro F1, and 59.11% balanced accuracy. The strongest predictors were Restless Without Social Media, Use Without Purpose, and Interest Fluctuation.Research implications – The findings are preliminary, dataset-specific, and based on a survey-derived composite label whose internal reliability still requires validation.Originality – This study contributes a leakage-controlled classification approach for analyzing attention-related survey categories.
Mining for Success: Personalized Learning Paths through Educational Data Analysis Delima Sitanggang; Rijois Iboy Erwin Saragih
International Journal of Information System and Innovative Technology Vol. 2 No. 2 (2023): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/dk592976

Abstract

This research addresses the research problem of leveraging educational data mining techniques to create personalized learning paths for individual students based on their unique learning styles, preferences, and strengths. The study aims to investigate whether tailoring educational experiences using mined data leads to enhanced academic performance, increased engagement, and a more positive learning experience compared to traditional, non-personalized approaches. Methodologically, the research employs data-driven analyses of diverse student datasets, integrating variables such as academic performance, learning preferences, and individual strengths. The study explores the effectiveness of personalized learning paths facilitated by educational data mining in various educational settings. Ethical considerations related to data privacy and responsible use of mined information are also systematically addressed throughout the research. Results from this research contribute valuable insights into the efficacy of personalized learning strategies driven by data mining, offering implications for educational practices and curriculum design. The study provides a comprehensive examination of the benefits and challenges associated with implementing personalized learning paths, and the results have the potential to inform educational institutions, policymakers, and educators on optimizing learning environments.
Classification Of Hypertension Using K-Nearest Neighbor Based On Photoplethysmograph Data And Blood Pressure Estimator Jasmin William Natanael Sinaga; Tasya Rouli Christy Tampubolon; Ester Farida Simanjuntak; Delima Sitanggang; Reyhan Achmad Rizal
Journal of Innovation and Technology Polbeng Series on Informatics (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.
Co-Authors -, Amalia ., Calvin ., Efendy ., Kelvin Abdi Dharma Abdi Dharma Achmad Ridwan 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 Darren Lucius David David Debby Novriyanti Br Tp.Bolon Diasta Natanael Sembiring Ester Farida Simanjuntak 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 Immanuel Sinaga, Ferdy Indra, Evta Indren, Indren Intan Susanti Simarmata Jasmin William Natanael Sinaga Jefri Syah Putra Laoli Jorgi L.Tobing, Stefanus Juan Juanta, Palma Julio Putra Tarigan 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 Palma Juanta Pamungkas, William Aldo Perangin Angin, Despaleri Perangin-angin, Despaleri Pungki Laurensius Ritonga Putra, Muhammad Amsar Reyhan Achmad Rizal Reza Pahlevi Rijois I. E. Saragih Rinaldi Fauzan 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 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 Tasya Rouli Christy Tampubolon Tifanny Tifanny Togar Timoteus Gultom Wijaya, Bryan Wilbert Solo, Eddrick Winarti Pasaribu Yennimar Yennimar, Yennimar Yoga Tri Nugraha Yonata Laia Yosua Morales Saragi Yumna, Farhan Zachary Djuli