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All Journal Dinamik Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURTEKSI JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) METIK JURNAL Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam 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 Mechanical Engineering Nursing Physics Public Health Social Sciences

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Digital Signs Security System using AES-Blowfish-RSA Hybrid Cryptography Approach HS, Christnatalis; Husein, Amir Mahmud
Sinkron : jurnal dan penelitian teknik informatika Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (921.482 KB) | DOI: 10.33395/sinkron.v4i1.10244

Abstract

Increasing application of digital signatures in legitimate authentication of administrative documents in both public and private environments is one of the points of concern, especially the issue of security and integrity of ownership of signatures. Digital signature is a mathematical scheme, which a unit to identify and prove the authenticity of the owner of the message or document. The study aims to analyze security patterns and identification of digital signatures on documents using the RSA-AES-Blowfish hybrid cryptographic method approach for securing digital signatures, while the Kohonen SOM method is applied to identify ownership recognition of signature images. The analysis framework used in this study is each signature will be stored in the form of a digital image file that has been encrypted using hybrid method of AES-Blowfish with the SHA 256 hash function. Process of forming private keys and public keys in the signature image using the RSA algorithm. Authentic verification of the use of digital signatures on the document has 2 (two) stages, the first stage is signature will be valid used on the document if the result of hashing the selected signature image is the same based on the private key and public key entered by the user, while the second stage identification is done using the Kohonen SOM method to validate the similarity of the chosen signature with the ownership of the signature.
Analysis of Facial Image Extraction on Facial Recognition using Kohonen SOM for UNPRI SIAKAD Online User Authentication Rizal, Reyhan Achmad; HS, Christnatalis
Sinkron : jurnal dan penelitian teknik informatika Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (443.265 KB) | DOI: 10.33395/sinkron.v4i1.10242

Abstract

Academic Information System (Sistem Informasi Akademik aka SIAKAD) Online of Universitas Prima Indonesia (UNPRI) is one of the applications used to facilitate the administration process of lectures which includes the filling process of study plan cards (Kartu Rencana Studi aka KRS), study result cards (Kartu Hasil Studi aka KHS), class schedules, submission of research titles, seminars, and other processes. SIAKAD UNPRI can be accessed by students, lecturers, and academics where every user has a password that has been encrypted to maintain the security of information from people who are not responsible, password security using the encryption method needs to be changed regularly, but there are still many students, lecturers and academic community who are reluctant to change passwords. To improve the security verification stage for SIAKAD users, we propose a face recognition feature approach. Face recognition is a feature that allows the identification of someone from a digital image or video. The way the facial recognition method works is by comparing face data from the camera or images with images that were previously stored in a database. In this study, the Kohonen SOM method is proposed for face identification based on the feature extraction approach of discrete cosine transform (DCT), linear discriminant analysis (LDA) and principal component analysis (PCA) to improve the security of UNPRI SIAKAD users. The analytical framework is done by requiring students to do face taking, where each student will save 5 (five) faces extracted with facial features using the DCT, LDA and PCA model approach, feature extraction results are used as input to the Kohonen SOM network for training and testing facial recognition, then analysis of the effect of DCT, LDA and PCA feature extraction on the Kohonen network on facial recognition accuracy.
COMPARATIVE COMPRESSION OF WAVELET HAAR TRANSFORMATION WITH DISCRETE WAVELET TRANSFORM ON COLORED IMAGE COMPRESSION Christnatalis, Christnatalis; Bachtiar, Bachtiar; Rony, Rony
JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (558.248 KB) | DOI: 10.31289/jite.v3i2.3154

Abstract

In this research, the algorithm used to compress images is using the haar wavelet transformation method and the discrete wavelet transform algorithm. The image compression based on Wavelet Wavelet transform uses a calculation system with decomposition with row direction and decomposition with column direction. While discrete wavelet transform-based image compression, the size of the compressed image produced will be more optimal because some information that is not so useful, not so felt, and not so seen by humans will be eliminated so that humans still assume that the data can still be used even though it is compressed. The data used are data taken directly, so the test results are obtained that digital image compression based on Wavelet Wavelet Transformation gets a compression ratio of 41%, while the discrete wavelet transform reaches 29.5%. Based on research problems regarding the efficiency of storage media, it can be concluded that the right algorithm to choose is the Haar Wavelet transformation algorithm. To improve compression results it is recommended to use wavelet transforms other than haar, such as daubechies, symlets, and so on.
Comparative Compression of Wavelet Haar Transformation with Discrete Wavelet Transform on Colored Image Compression Christnatalis Christnatalis; Bachtiar Bachtiar; Rony Rony
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v3i2.3154

Abstract

In this research, the algorithm used to compress images is using the haar wavelet transformation method and the discrete wavelet transform algorithm. The image compression based on Wavelet Wavelet transform uses a calculation system with decomposition with row direction and decomposition with column direction. While discrete wavelet transform-based image compression, the size of the compressed image produced will be more optimal because some information that is not so useful, not so felt, and not so seen by humans will be eliminated so that humans still assume that the data can still be used even though it is compressed. The data used are data taken directly, so the test results are obtained that digital image compression based on Wavelet Wavelet Transformation gets a compression ratio of 41%, while the discrete wavelet transform reaches 29.5%. Based on research problems regarding the efficiency of storage media, it can be concluded that the right algorithm to choose is the Haar Wavelet transformation algorithm. To improve compression results it is recommended to use wavelet transforms other than haar, such as daubechies, symlets, and so on.
APLIKASI GAME BATTLE PUZZLE DENGAN METODE BEST FIRST SEARCH Aqil Muhammad Arviansyah; Christnatalis Christnatalis
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 2 No. 1 (2019): Jutikomp Volume 2 Nomor 1 April 2019
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v2i1.563

Abstract

Perancangan aplikasi ini akan membuat suatu game berbentuk battle puzzle. Penelitian ini bertujuan untuk merancang konten sebuah game yang mendidik dan tetap bisa diterima oleh masyarakat, dimana penelitian ini menggunakan metode Best First Search serta menggunakan software microsoft visual basic.net 2008 dalam perancangannya. Metode Best First Search akan mencari ruang keadaan yang paling tepat untuk mencapai solusi permasalahan yang dapat diterima dan mempercepat proses kerjanya. Penelitian ini akan sangat bermanfaat bagi masyarakat khususnya anak-anak agar pengguna dapat meningkatkan pengetahuan dan wawasan serta menjadi bahan evaluasi intelligence pengguna
DATA MINING ALGORITHM C4.5 CLASSIFICATION DETERMINATION CREDIT ELIGIBILITY FOR JAYA BERSAMA COOPERATIVES (KORJABE) Christnatalis Christnatalis; Roni Rayandi Saragih; Bobby Christianto Tambunan
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 8, No 1 (2021): Desember 2021
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v8i1.1298

Abstract

Abstract: This study uses the C4.5 classification algorithm to determine creditworthness, clasification aims to divide the assigned object intoin a number of categories called classes. In this study, the authorusing data mining and C4.5 algorithm as the selection method. The criteria used are loan installments, prospective customer income, termloan time, status of prospective customers. This study resulted in a classification modeldecision tree using the C4.5 algorithm is included in the Excellent category Classification with an accuracy value of 98.33% and a classification error of 1.67%,so that this study uses 70% training data and 30% test data. From resultthe calculation obtained shows that the C4.5 algorithm can be usedto determine the feasibility of granting credit to Koperasi Jaya customers Together (KORJABE).            Keywords: Analysis, Credit Eligibility, C4 Algorithm, Data Mining, Method  Abstrak: Penelitian ini menggunakan metode Algoritma C4.5 klasifikasi untuk menentukan kelayakan kredit, klasifikasi bertujuan untuk membagi objek yang ditetapkan ke dalam satu  nomor kategori yang disebut kelas. Dalam penelitian ini, penulis menggunankan data mining dan algoritma C4.5 sebagai metode pemilihannya. Kriteria yang digunakan yaitu , angsuran  pinjaman,penghasilan calon nasabah,jangka waktu pinjaman ,status calon nasabah. Penelitian ini menghasillkan model klasifikasi pohon keputusan menggunakan algoritma C4.5 termasuk dalam kategori Excellent Classification dengan nilai akurasi sebesar 98,33% dan klasifikasi eror 1,67%, sehingga penelitian ini kan menggunakan data latih 70% dan data uji 30%. Dari hasil perhitungan yang diperoleh menunjukan bahwa algoritma C4.5 dapat digunakan untuk menen tukan kelayakan pemberian kredit kepada nasabah Koperasi Jaya Bersama (KORJABE). Kata kunci: Algoritma C4.5, Analisis,  Data Mining, Kelayakan Kredit, Metode
PERBANDINGAN EFEKTIFITAS ALGORITMA DECISSION TREE, NAÏVE BAYES, K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE DALAM MELAKUKAN KLASIFIKASI Ervin Susanto Gulo; Christnatalis -; Yosafat Ricardo Gulo; Sarinova Florina Marbun
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 5 No. 2 (2022): Jutikomp Volume 5 Nomor 2 Oktober 2022
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v5i2.2940

Abstract

Klasfikasi merupakan kasus yang sering diangkat menjadi judul penelitian dikarenakan banyaknya metodeyang bisa melakukan klasifikasi. Adapun beberapa metode yang bisa melakukan klasifikasi adalah metodeDecission Tree, K-Nearest Neighbor, Naïve Bayes dan Support Vector Machine. Metode-metode tersebutmemiliki kelebihan dan kekurangannya tersendiri, oleh karena itu setiap metode menghasilkan nilai akurasiyang berbeda-beda. Dari 40 data jurnal penelitian yang sudah dilakukan sebelumnya didapatkan hasil berupametode Support Vector Machine mendapatkan nilai rata-rata akurasi 86,61% dan menjadi metode dengannilai akurasi tertinggi dibandingkan ketiga metode lainnya. Metode Naïve Bayes mendapatkan nilai hasilrata-rata akurasi sebesar 73% dan menjadi metode dengan nilai akurasi terendah dibandingkan ketigametode lainnya
ANALISIS PELAYANAN RUMAH SAKIT UMUM DENGAN PERBANDINGAN ANTARA METODE ALGORITMA KMEANS, DAN K-MEDOIDS CLUSTERING Christnatalis -; Eric Claudyo; Lucky -; Herry Kristover Manullang; Arus Iman Zebua
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 6 No. 2 (2023): Jutikomp Volume 6 Nomor 2 Oktober 2023
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v6i2.4145

Abstract

Hospitals are part of a health system that aims to provide comprehensive individual health services. Services include inpatient, outpatient, and emergency care. Hospitals must provide high-quality services according to existing standards and serve all levels of society. Understanding customers' needs, wants, and demands will provide Another factor that affects the quality of health services is the availability of resources and service facilities during the insurance period. Lack of continuity of service affects the efficiency and quality of the relationship. The quality of health services is also strongly influenced by the ease of information and timeliness of public hospital services. Based on data from 341 hospital service quality surveys, the results obtained from the K-Means algorithm method are 2.701288, while the results obtained in the K-Methoids Clustering method are 2.17, where the General Hospital service questionnaire is a very satisfied category. A comparison of the results of the K-Means and K-Medoids methods aims to make it easier to measure customer satisfaction.
Klasifikasi Penyakit Pada Baglog Jamur Tiram Menggunakan Metode Convolutional Neural Network Christnatalis, Christnatalis; Sozaro Lase, Christoper Darius; Sitompul, Toga Hasudungan; Hondro, Anugrah Prasakti
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.480

Abstract

Penelitian ini membahas permasalahan penyakit yang menyerang baglog jamur tiram, yang dapat menyebabkan penurunan kualitas dan kuantitas hasil panen. Tujuan utama penelitian ini adalah mengembangkan sistem klasifikasi penyakit pada baglog jamur tiram menggunakan algoritma Convolutional Neural Network , sehingga memungkinkan deteksi penyakit secara cepat dan akurat. Dataset gambar baglog jamur tiram yang terkena penyakit dibagi menjadi 80% untuk data training dan 20% untuk data validation. Teknik transfer learning diterapkan untuk memanfaatkan fitur-fitur dari model pra-terlatih guna meningkatkan efisiensi pelatihan dan akurasi prediksi. Transfer learning adalah teknik dalam machine learning di mana model yang telah dilatih pada satu tugas digunakan kembali sebagai titik awal untuk tugas lain yang serupa. Proses pelatihan model dilakukan sebanyak 5 kali percobaan, masing-masing dengan 25 epoch dan mendapatkan model terbai di epoch ke 8 . Melakukan beberapa percobaan dengan berbagai konfigurasi dan mengulangi proses pelatihan beberapa kali membantu memastikan bahwa hasil yang diperoleh stabil dan tidak disebabkan oleh kebetulan. Hasil penelitian menunjukkan bahwa model yang dikembangkan memiliki kinerja yang sangat baik dan konsisten, dengan akurasi validation yang stabil sebesar 97.14% dan nilai loss pada validation sebesar 0.0893. Akurasi validation menunjukkan persentase prediksi yang benar pada data validation, sementara nilai loss mengindikasikan seberapa baik model meminimalkan kesalahan prediksi.
Systematic Literature Review: Penggunaan Sensor dalam Deteksi Nyeri Wajah berdasarkan Database Publik Azibi, Ahmad Izzu; Hutabarat, Emy Priyanka; Tarigan, Juan Kevin Timothi; Sitorus, Zeremia Armando; HS, Christnatalis
Dinamik Vol 30 No 2 (2025)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v30i2.10282

Abstract

Deteksi nyeri objektif merupakan tantangan dalam dunia medis, terutama bagi pasien yang tidak mampu mengungkapkan rasa sakit secara verbal. Dengan kemajuan teknologi sensor dan kecerdasan buatan, sistem otomatis untuk mendeteksi nyeri berbasis sinyal fisiologis dan ekspresi wajah mulai dikembangkan. Studi ini bertujuan mengidentifikasi tren, metode, dan kualitas metodologis dari penelitian yang menggunakan database publik seperti BioVid Heat Pain, UNBC-McMaster, dan SenseEmotion dalam pengembangan sistem deteksi nyeri berbasis sensor. Penelitian dilakukan dengan pendekatan Systematic Literature Review (SLR) berdasarkan protokol PRISMA 2020 melalui pencarian artikel di Google Scholar dalam rentang tahun 2015–2024. Setelah seleksi berdasarkan kriteria inklusi dan eksklusi, 26 studi dimasukkan ke dalam sintesis naratif. Data dianalisis berdasarkan jenis sensor, metode algoritma, akurasi, dan ukuran sampel, serta dievaluasi menggunakan pendekatan GRADE. Hasil menunjukkan bahwa BioVid dan UNBC-McMaster adalah database paling sering digunakan, dengan sensor EDA, EMG, dan ekspresi wajah sebagai modalitas dominan. Metode klasifikasi umum mencakup CNN, SVM, dan Random Forest. Studi menyimpulkan bahwa pendekatan multimodal dan deep learning meningkatkan akurasi deteksi nyeri, namun validasi klinis dan perhatian terhadap keragaman demografis masih dibutuhkan.
Co-Authors Achmad Ridwan, Achmad Agung Prabowo Amir Mahmud Husein, Amir Mahmud Andrew Andrew, Andrew Andrian Andrian Andy Andy Aqil Muhammad Arviansyah Arus Iman Zebua Azibi, Ahmad Izzu Bachtiar Bachtiar Bachtiar Bachtiar Bobby Christianto Tambunan Br Sitohang, Sondang Agustina Darmanto Darmanto Daya, Onita Delima Sitanggang, Delima Eric Claudyo Ervin Susanto Gulo Felim, Filbert Ginting, Riski Titian Herry Kristover Manullang Hondro, Anugrah Prasakti Hutabarat, Emy Priyanka Kenichiro, Yoshie Kristanti, Inka Kuantan, Steven Lucky - Manday, Dhanny Rukmana Margaretha, Yulia Perangin Angin, Despaleri Rinaldi Rinaldi Rizal, Reyhan Achmad Roni Rayandi Saragih Rony Rony Rony, Rony Sarinova Florina Marbun Seteven, Billie Setia, Rifin 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 Yennimar, Yennimar Yosafat Ricardo Gulo