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All Journal Dinamik Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jiko (Jurnal Informatika dan komputer) JURTEKSI JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) METIK JURNAL Jurnal Pendidikan dan Teknologi Indonesia Jurnal Minfo Polgan (JMP) Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Jurnal Algoritma JURNAL TEKNOLOGI, KESEHATAN DAN ILMU SOSIAL
HS, Christnatalis
Universitas Prima Indonesia
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences

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Systematic Literature Review: Menilai Tingkat Nyeri Melalui Pola Suara Kristanti, Inka; Br Sitohang, Sondang Agustina; Margaretha, Yulia; Daya, Onita; HS, Christnatalis
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mn6wfj51

Abstract

In the medical field, Accurate detection of pain levels is a crucial aspect of healthcare, especially for patient groups who cannot directly communicate their pain, such as infants, individuals in critical condition, or those with neurological dysfunction. This study aims to test the effectiveness of a voice pattern analysis approach in detecting pain levels through a Systematic Literature Review (SLR) method. From 500 articles, 13 relevant inclusion studies were selected based on PRISMA criteria. The review results indicate that sounds such as crying and moaning can serve as objective pain indicators, and have great potential for integration into clinical systems. Supported by artificial intelligence algorithms such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), the accuracy level of pain detection based on sound reaches 83% to 96% depending on the type of data and methods. Although the results are promising, there are several challenges such as limited dataset variability, background noise interference, and the absence of a standardized voice-based pain classification. Therefore, further research is needed for direct validation of the system in clinical environments, development of classification standards, and exploration of multimodality to improve accuracy. This research is expected to serve as a foundation for the development of more objective, adaptive, and inclusive pain assessment technologies for patients with communication limitations.
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.
PERBANDINGAN METODE MULTIPLICATIVE, ADDITIVE DAN DOUBLE SEASONAL HOLT-WINTERS UNTUK PREDIKSI PENJUALAN MOBIL Christnatalis, Christnatalis; Rinaldi, Rinaldi; Andy, Andy; Seteven, Billie; Darmanto, Darmanto; Sitorus, Daniel Ganda
JURNAL TEKNOLOGI KESEHATAN DAN ILMU SOSIAL (TEKESNOS) Vol. 1 No. 1 (2019): JURNAL TEKNOLOGI, KESEHATAN DAN ILMU SOSIAL (TEKESNOS)
Publisher : Universitas Sari Mutiara Indonesia

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

Abstract

Prediksi (forecasting) dapat membantu perusahaan untuk menentukan jumlah penjualan barang di masa yang akan datang, sehingga perusahaan dapat memutuskan untuk melakukan penambahan atau pengurangan stok barang. Metode Holt-Winters adalah metode prediksi kuantitatif yang digunakan untuk memprediksi data tren dan musiman. Beberapa variasi dari metode Holt-Winters adalah Multiplicative Holt-Winters, Additive Holt-Winters dan Double Seasonal Holt-Winters. Penerapan beberapa variasi Holt-Winters pada data penjualan mobil GAIKINDO akan memperoleh informasi mengenai akurasi dari ketiga metode. Akurasi metode peramalan Holt-Winters bergantung pada model data yang digunakan. Additive Holt-Winters cocok digunakan untuk memprediksi model data yang cukup konstan, seperti data penjualan mobil Toyota (MAPE = 3.18278% dan nilai RMSE = 1304.96), sedangkan Double Seasonal Holt-Winters cocok digunakan untuk model data penjualan yang mempunyai dua pola musiman, seperti data penjualan mobil BMW serta metode Multiplicative Holt-Winters cocok untuk model data penjualan mobil yang mempunyai fluktuasi cukup tinggi di atas dan di bawah nilai rata-rata, seperti data mobil Scania.
PENGACAKAN CITRA DIGITAL DENGAN MENGGUNAKAN LOGISTIC MAP DAN PIECEWISE LINEAR CHAOTIC MAP Andrew, Andrew; Andrian, Andrian; Kuantan, Steven; Setia, Rifin; Christnatalis, Christnatalis
JURNAL TEKNOLOGI KESEHATAN DAN ILMU SOSIAL (TEKESNOS) Vol. 1 No. 1 (2019): JURNAL TEKNOLOGI, KESEHATAN DAN ILMU SOSIAL (TEKESNOS)
Publisher : Universitas Sari Mutiara Indonesia

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

Abstract

Transfer file citra dapat dilakukan melalui jaringan internet yang tidak aman, sehingga citra pada bidang medis, penelitian, bisnis, militer dan bidang lainnya yang mengandung kerahasiaan membutuhkan proteksi. Pengacakan citra dapat dilakukan untuk mengamankan informasi yang terkandung pada citra digital.Metode chaotic map yang dapat digunakan untuk mengacak citra digital adalah Logistic Map dan Piecewise Linear Chaotic Map. Kedua metode chaotic map ini diketahui memiliki siklus yang besar dan memiliki distribusi keseragaman yang acak.Pengacakan citra dengan menggunakan kedua chaotic map ini adalah dengan mengacak nilai intensitas piksel citra menggunakan fungsi gerbang logika xor dan nilai acak yang dihasilkan oleh kedua metode chaotic map.Pengacakan dengan Logistic Map dengan menggunakan nilai parameter = 4 memperoleh hasil pengacakan citra paling baik secara visual dengan nilai MSE = 9496.75862, sedangkan pengacakan dengan Piecewise Linear Chaotic Map dengan menggunakan nilai parameter p = 0.5 memperoleh hasil pengacakan citra paling baik secara visual dengan nilai MSE = 9803.35705.
Tinjauan Sistematis Teknologi Radar Mimo dan Kecerdasan Buatan untuk Deteksi Nyeri Non-Invasif Lintas Populasi Wijaya, Sky Xavier; Kenichiro, Yoshie; Felim, Filbert; HS, Christnatalis; Prabowo, Agung
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10361

Abstract

Deteksi nyeri secara objektif merupakan tantangan penting dalam dunia medis, terutama bagi pasien yang tidak mampu menyampaikan rasa sakitnya secara verbal, seperti bayi, lansia, atau penderita gangguan komunikasi. Teknologi non- invasif berbasis sensor menjadi solusi potensial untuk mengatasi keterbatasan metode subjektif. Penelitian ini bertujuan meninjau secara sistematis literatur terkini mengenai penerapan Radar MIMO dan algoritma kecerdasan buatan dalam deteksi nyeri non-invasif. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan pedoman PRISMA 2020, melalui penelusuran basis data IEEE Xplore, ScienceDirect, PubMed, Google Scholar, dan SpringerLink untuk periode 2021– 2025. Dari hasil seleksi diperoleh 17 artikel inklusi yang mencakup penggunaan Radar MIMO, UNBC-McMaster, BioVid, Medical Imaging (CT/MRI), Radar SISO, serta studi review, survey, bibliometrik, dan teoretis. Dari sisi algoritma, CNN dan SVM menjadi pendekatan paling dominan, diikuti Neural Network dan metode lain, dengan tren yang mengarah pada penggunaan multimodal untuk meningkatkan akurasi. Hasil penilaian kualitas dengan GRADE menunjukkan mayoritas studi berkualitas sedang, dengan keterbatasan utama pada ukuran sampel kecil, pelabelan nyeri yang belum konsisten, bias populasi, serta kurangnya validasi klinis nyata. Kesimpulannya, Radar MIMO dan algoritma deep learning memiliki potensi besar untuk deteksi nyeri non-invasif. Namun, penelitian lanjutan perlu difokuskan pada pembangunan dataset yang lebih inklusif, standarisasi pelabelan nyeri, serta pengujian dalam konteks klinis, dengan memperhatikan aspek etika dan privasi agar teknologi ini dapat diimplementasikan secara luas dalam layanan kesehatan.
EVALUASI STABILITAS ALGORITMA SHA-256, BLAKE2, WHIRLPOOL, SKEIN TERHADAP VARIASI KARAKTERISTIK DATA Yahya Siregar; Rohid Syavelen Pahti; Habibi; Christnatalis HS
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7796

Abstract

Cryptographic hash algorithms play a crucial role in maintaining data integrity and security, particularly in systems processing large amounts of diverse data. However, algorithm selection is often based solely on standards and adoption rates without considering performance stability against variations in data characteristics. This study aims to assess the stability of the SHA-256, BLAKE2, Whirlpool, and Skein algorithms against variations in multimedia data, specifically text, images, music, and video, through two main dimensions: Resource Stability and Statistical Stability. A benchmarking-based computational approach was employed by measuring CPU, memory, latency, and throughput usage over ten runs. The stability level is described using the Coefficient of Variation (CV) to measure the relative variation between runs. The test results show that Whirlpool has the most consistent Resource Stability level with the lowest average CV values ​​for CPU (1.86%), memory (6.67%), latency (2.66%), and throughput (3.04%), which indicates the most stable performance compared to other algorithms. In terms of statistical stability, all algorithms exhibit an avalanche effect approaching ideal conditions (≈50%), an even distribution of hexadecimal outputs, and a bit ratio approaching 50:50 without significant bias. No pure cryptographic collisions were found; hash value similarities were detected by duplicate dataset content. The results confirm that the selection of a hash algorithm should consider the stability of resource utilization in addition to security aspects, especially in systems with high computational loads and diverse data characteristics.  
Analisis Konseptual Pendeteksian Tingkat Rasa Sakit Pada Penderita Penyakit Jantung Koroner Berdasarkan Ekspresi Wajah Menggunakan Systematic Literature Review (SLR) Maruansa Iruanto Sianipar; Fredy Vico Ardian Purba; Daniel Roppu Ganda Panjaitan; Jeges Martunas Manik; Christnatalis HS
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3557

Abstract

Pain assessment is a critical aspect of healthcare delivery, particularly for patients with Coronary Artery Disease (CAD), who often experience communication difficulties during acute episodes. This study aims to analyze the current state of research on facial expression-based pain detection through a Systematic Literature Review (SLR) conducted in accordance with the PRISMA guidelines. A total of 86 articles published between 2020 and 2025 were retrieved from the Google Scholar database and systematically reviewed. The critical synthesis reveals that studies specifically investigating facial expressions for pain assessment in patients with coronary artery disease remain highly limited. Existing research is predominantly focused on the development of pain detection technologies for general clinical settings, including the use of physiological indicators, experimental datasets such as the BioVid Heat Pain Database, and the application of generic machine learning algorithms. The primary scientific contribution of this review is the identification of a substantial methodological gap within the current literature. Existing studies are largely characterized by descriptive qualitative research designs and a strong reliance on secondary laboratory datasets rather than real-world clinical data collected from patients. The novelty of this study lies in its comprehensive mapping of the existing literature and its proposal to adapt generic pain detection models to the specific characteristics of chest pain (angina pectoris) experienced by patients with coronary artery disease. The findings highlight the need for future research to shift toward integrating computational methods with real-world clinical data from cardiology patients in order to improve the external validity and practical applicability of pain detection systems within actual healthcare environments.
SYSTEMATIC REVIEW: COMPARISON OF ARTIFICIAL INTELLIGENCE METHODS FOR PAIN DETECTION Fiona Angeline; Sherli Sherli; Devin Tanadi; Rendi Winata; Christnatalis HS
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 2 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i2.11648

Abstract

Objective pain detection remains a major challenge in healthcare because conventional assessments rely on subjective self-reports. Advances in artificial intelligence (AI) have enabled more objective approaches by analyzing biological signals and human expressions. Facial expressions and functional Near-Infrared Spectroscopy (fNIRS) are widely studied due to their complementary characteristics. Facial expressions are non-invasive, easy to capture, and strongly associated with visible pain responses, making them suitable for real-time applications. In contrast, fNIRS measures brain activity related to pain perception, providing a more objective physiological perspective. This study follows PRISMA guidelines for systematic literature review. Studies were identified from major databases, screened for relevance, assessed for eligibility, and included for final analysis. A total of 45 studies were selected. Previous research shows that AI, especially deep learning, is effective in analyzing facial expressions and fNIRS signals for pain detection. However, few studies systematically compare these modalities in a unified framework. This review highlights a shift from single-modality to hybrid and multimodal approaches integrating facial and fNIRS data. Deep learning models, particularly CNNs for facial analysis and hybrid machine learning–deep learning methods for physiological signals, dominate recent studies. Multimodal fusion consistently outperforms single-modality approaches, improving accuracy and robustness in pain detection tasks
Evaluasi Desain Basis Data Relasional untuk Sistem Distribusi Tabung Gas Menggunakan Identifikasi Salsa Tondang; Elicia S A Br. Sihombing; Anisa Auliza Siregar; Christnatalis HS
Jurnal Minfo Polgan Vol. 15 No. 2 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i2.16406

Abstract

Sistem distribusi tabung gas memerlukan basis data yang mampu mendukung pengelolaan inventori dan pelacakan aset secara akurat. Namun, banyak sistem masih menggunakan pendekatan agregatif yang hanya mencatat jumlah tabung berdasarkan jenis gas tanpa identifikasi unik pada setiap aset. Kondisi tersebut menyebabkan keterbatasan dalam proses pelacakan, audit inventori, dan pengendalian distribusi. Penelitian ini bertujuan mengevaluasi desain basis data relasional berbasis identifikasi unik menggunakan barcode dibandingkan desain agregatif pada sistem distribusi tabung gas. Metode penelitian dilakukan melalui perancangan dua model basis data relasional, yaitu desain agregatif dan desain berbasis barcode, yang kemudian dievaluasi menggunakan lima skenario benchmark query (Q1–Q5), pengukuran throughput, analisis penggunaan sumber daya sistem (resource usage), serta evaluasi kemampuan pelacakan aset dan kejelasan informasi. Hasil penelitian menunjukkan bahwa desain berbasis barcode secara umum menghasilkan waktu eksekusi query yang lebih cepat dan throughput yang lebih tinggi dibandingkan desain agregatif. Analisis resource usage menunjukkan bahwa penerapan identifikasi unik tidak menyebabkan penurunan kinerja sistem secara signifikan. Selain itu, desain berbasis barcode mampu menyediakan informasi identitas tabung, status distribusi, dan riwayat transaksi aset secara individual yang tidak tersedia pada desain agregatif. Berdasarkan hasil evaluasi, desain basis data berbasis barcode terbukti lebih efektif dalam mendukung pelacakan aset, pengelolaan inventori, dan akses informasi pada sistem distribusi tabung gas.
Co-Authors Achmad Ridwan Agung Prabowo Amir Mahmud Husein, Amir Mahmud Andrew Andrew, Andrew Andrian Andrian Andy Andy Anisa Auliza Siregar Aqil Muhammad Arviansyah Arus Iman Zebua Azibi, Ahmad Izzu Bachtiar Bachtiar Bachtiar Bachtiar Bobby Christianto Tambunan Br Sitohang, Sondang Agustina Daniel Roppu Ganda Panjaitan Darmanto Darmanto Daya, Onita Delima Sitanggang, Delima Devin Tanadi Elicia S A Br. Sihombing Eric Claudyo Ervin Susanto Gulo Felim, Filbert Fiona Angeline Fredy Vico Ardian Purba Ginting, Riski Titian Habibi Herry Kristover Manullang Hondro, Anugrah Prasakti Hutabarat, Emy Priyanka Jeges Martunas Manik Kenichiro, Yoshie Kristanti, Inka Kuantan, Steven Lucky - Manday, Dhanny Rukmana Margaretha, Yulia Maruansa Iruanto Sianipar Perangin Angin, Despaleri Rendi Winata Rinaldi Rinaldi Rizal, Reyhan Achmad Rohid Syavelen Pahti Roni Rayandi Saragih Rony Rony Rony, Rony Salsa Tondang Sarinova Florina Marbun Seteven, Billie Setia, Rifin Sherli Sherli Siregar, Saut Dohot Sitompul, Toga Hasudungan Sitorus, Daniel Ganda Sitorus, Zeremia Armando Sozaro Lase, Christoper Darius Tarigan, Juan Kevin Timothi Togar Timoteus Gultom Wijaya, Sky Xavier Yahya Siregar Yennimar, Yennimar Yosafat Ricardo Gulo