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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jusikom: Jurnal Sistem Informasi Ilmu Komputer INTI Nusa Mandiri REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Brilliance: Research of Artificial Intelligence Sisfo: Jurnal Ilmiah Sistem Informasi The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Rizal, Reyhan Achmad
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Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Mechanical Engineering Other

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Klasifikasi Wajah Menggunakan Support Vector Machine (SVM) Rizal, Reyhan achmad; Girsang, Imron Sanjaya; Prasetiyo, Sidik Apriyadi
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 3 No. 2 (2019): Remik Volume 3 Nomor 2 April 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.501 KB) | DOI: 10.33395/remik.v3i2.10080

Abstract

Klasifikasi wajah merupakan teknik yang dapat digunakan untuk membedakan karakteristik pola wajah seseorang. Sistem klasifikasi wajah adalah suatu aplikasi yang membuat sebuah mesin dapat mengenali wajah seseorang sesuai dengan citra wajah yang telah ditraining dan disimpan di dalam database mesin tersebut. Klasifikasi wajah sendiri dapat dilakukan dengan berbagai cara, salah satunya adalah menggunakan metode support vector machine (SVM). Penelitian ini dilakukan dengan sampling yang di ambil dalam variasi posisi pada sudut kemiringan subjek (-90°, -70°, -45°, -25°, -5° ) dan (+90°, +70°, +45°, +25°, +5° ) dengan ukuran citra 640x480. Sistem klasifikasi wajah didalam penelitian ini dibangun dengan menggunakan metode support vector machine (SVM) dan bahasa pemograman Matlap. Penelitian ini menghasilkan tingkat true detection 90% dan false detection 10% dari jumlah sampel 200 subjek yang digunakan. Keywords— Klasifikasi wajah, sudut kemiringan, SVM
Klasifikasi Kandungan Boraks Dalam Saos Tomat Melalui Citra Menggunakan GLCM Rizal, Reyhan Achmad; Susanto, Mario; Chandra, Andy
Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (208.355 KB) | DOI: 10.33395/sinkron.v4i2.10508

Abstract

One of the food products that need to be reviewed for safety and is the most consumed is tomato sauce, although it contains a large amount of water in the sauce which has a long shelf life because it contains acid, sugar, salt, and is often given preservatives. The purpose of this study was to determine the tomato sauce using harmful preservatives such as the addition of borax. The dataset used in this study is the image of tomato sauce containing borax and not with the number of samples 400 images of tomato sauce with different comparison percentages starting from the image of tomato sauce with 70% borax content, image of tomato sauce with 50% borax content, image tomatoes with 30% borax content and image of tomato sauce that does not contain borax. A sampling of images using a camera phone brand xiaomi note 5 by mixing borax in the original sauce before the sample is used for the training and testing process. The classification results show the gray level co-occurrence matrix (GLCM) method is quite optimal in classifying tomato sauce data containing borax and not with an average percentage of the introduction of 88%.
Comparison of Machine Learning Classification Algorithms in Sentiment Analysis Product Review of North Padang Lawas Regency Yennimar, Yennimar; Rizal, Reyhan Achmad
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 (421.962 KB) | DOI: 10.33395/sinkron.v4i1.10416

Abstract

The growth of SMEs in Indonesia, which has increased by 6% every year, is driven by continued growth by many parties, including the government and private institutions that often conduct business coaching and assistance. Problems that are often encountered are the lack of willingness of MSME business practitioners to apply information technology and the internet, besides that most of them live in rural areas with very limited internet access and many are not yet digital-literate, adequate digital technology utilization capabilities and the will of business people For SMEs to understand customer needs, a service that is consistent with standard service procedures will give a good impression and pay attention to customer feedback. This research was conducted by collecting data on MSME products obtained from the North Padang Lawas District Trade Industry Office followed by the development of a Paluta Market website as a marketplace for media promotion and marketing of MSME products in North Padang Lawas by applying a sentiment analysis approach using machine learning classification algorithm to produce product rating values based on public opinion of MSME products contained on the website, in addition the system is able to classify consumer comment data on MSME products from various sources from the umkm web, so that it becomes useful information for MSME businesses especially in North Padang Lawas Regency and the community at large. The results of the application of sentiment analysis of a product on the Paluta Market website can be used as a reference in improving service and product quality, so as to create a variety of new opportunities that are profitable for MSME businesses.
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.
Aplikasi Sistem Penomoran Surat Otomatis Berbasis Website Pada Perumda Tirtanadi Aceh Utara Perolihin Manurung, Ericky Benna; Rizal, Reyhan Achmad
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 8 No. 1 (2024): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2024
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v8i1.18216

Abstract

Perumda tirtanadi aceh utara adalah salah satu instansi perusahaan air minum di kabupaten aceh utara. Perumda tirtanadi aceh utara sudah banyak memanfaatkan dunia teknologi dalam menyelesaikan pekerjaan sehari-sehari di kantor tersebut.  Namun untuk pengambilan nomor surat perumda tirtanadi aceh utara masih menggunakan sistem manual yaitu memanfaatkan buku agenda dalam pengambilan nomor surat sehingga hal ini membuat kinerjanya kurang efektif. Sehigga penulis tertarik melakukan penelitian didalam kantor tersebut. Pada saat melakukan pengumpulan data-data surat didalam kantor pusat perumda tirtanadi aceh utara melakukan 3 metode yaitu mengumpulkan dokumen, interview, dan melakukan observasi untuk mendapatkan hasil. Hal ini membuat penulis memanfaat perancangan sistem yang menggunakan DFD (Data Flow Diagram), ERD (Entity Relationship Diagram), serta flowchart. Selanjutnya dalam membuat sistem penulis memanfaatkan Sublime Text, MySQL Database Xampp, back-endnya menggunakan PHP biasa dan untuk tampilan website menggunakan Boostrapp 5. Penelitian ini diharapkan dapat mengahasilkan sebuah sebuah sistem penomoran surat masuk dan surat keluar secara otomatis berbasis web yang sangat bermanfaat untuk pegawai perumda tirtanadi aceh utara.
PERBANDINGAN ALGORITMA YOLOV3 DAN YOLOV4 DALAM PENGELOMPOKAN UKURAN TELUR AYAM SECARA REAL TIME Sembiring, Lysheeba Abbygail; Manik, Brian Fernanda; Jonathan, Jovi; Giovano, Steven; Rizal, Reyhan Achmad
INTI Nusa Mandiri Vol 19 No 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5699

Abstract

The common problem currently faced by MSMEs producing chicken eggs is the difficulty in calculating the number of eggs and grouping egg sizes where everything is still done manually so that errors often occur and many entrepreneurs often experience losses. To improve and strengthen productivity, management, and marketing in this business, technological innovation is needed. This study aims to detect the number of eggs and group egg sizes based on their type using the Yolov3 and Yolov4 algorithms. Based on the results of the tests carried out, it shows that the Yolov3 and Yolov4 algorithms are able to detect chicken eggs in real time with the best accuracy value obtained by the Yolov3 algorithm. The comparison was carried out using 10 epoch tests with an F1-Score value of 0.89 where the F1-Score value approaching 1 indicates that the system performance has been running well. The results of this classification can be used to create a real time egg calculation application that can help calculate the number of eggs every day by each MSME.
Penerapan Machine Learning Clustering K-Means dan Linear Regression Dalam Penentuan Tingkat Resiko Tuberkulosis Paru Ula, Mutammimul; Zulfikri, Abdi; Ulva , Ananda Faridhatul; Rizal, Reyhan Achmad
The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i1.3162

Abstract

Penyakit tuberkulosis paru yang paling banyak disebarkan oleh bakteri Mycobacterium dan tipe penyakit ini menular pada bagian paru. tercatat pada tahun 2019 tercatat ada sekitar 400.000 jiwa penduduk Bireuen dengan jumlah kasus sekiar 755 jiwa yang menderita penyakit TB paru. Penelitian ini bertujuan untuk melihat daerah rawan terkena penyakit TB paru dan prediksi berdasarkan pertumbuhan penduduk. Data Penelitian diambil dari rumah sakit dr Fauziah Bireun Dinas kesehatan yang terdapat pada 17 Kecamatan. berdasarkan hasil tersebut terdapat hasil analisis dalam melihat daerah yang tersebar penyakit Tb Paru menggunakan algoritma K-Means dan Metode Clusterwise Regression. Hasil yang diperoleh dalam pengklusteran daerah rawan tuberkulosis paru yang terdapat dua daerah yang tergolong ke dalam cluster satu, enam daerah tergolong ke dalam cluster dua dan sembilan daerah tergolong ke dalam cluster tiga.Hasil prediksi dengan algoritma Regresi Linier 0,5740 dan hasil prediksi berpengaruh terhadap variabel lain adalah 9,481456817. Hasil penelitian ini dapat dijadikan rujukan dinas kesehatan dalam menindaklanjuti penyakit paru.
Real Time Chicken Egg Size Classification Using Yolov4 Algorithm Sandy, Cut Lika Mestika; Husna, Asmaul; Rizal, Reyhan Achmad
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4496

Abstract

The common problem currently faced by MSMEs producing chicken eggs is experiencing difficulties in grouping egg sizes every day. Currently, grouping egg sizes is still done manually, this is less than optimal and prone to errors so that many business owners often experience losses. Grouping egg sizes before being sold is very important to note because each size affects the selling price of eggs. The use of technology on a MSME scale in laying hen farmers has not been widely adopted, this is due to limited access and understanding of technology so that to improve and strengthen productivity, management, and marketing in this business, technological innovation is needed. One alternative solution to deal with this problem is to build a real-time computerized system that can group eggs according to their size. This study aims to evaluate the performance of the Yolov4 algorithm in grouping egg sizes based on their size in real time. Based on the results of the tests carried out, the Yolov4 algorithm is able to group chicken eggs in real time with an F1-Score value: 0.89 where the F1-Score value approaching 1 indicates that the system performance has been running well. The results of this classification can be used to create a real-time egg size grouping application that can help MSMEs to monitor the productivity of chicken eggs every day.
COMPARISON OF SINGLE EXPONENTIAL SMOOTHING METHOD WITH DOUBLE EXPONENTIAL SMOOTHING METHOD PREDICTION OF SALT SALES Harly, Jesslyn; Nababan, Marlince; Bintang, Lidya Haryati; Rizal, Reyhan Achmad; -, Aisyah
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.v6i2.3366

Abstract

Predicting the quantity of product sales in the future aims to control the amount of existing product stock, so that the shortage or excess of product stock can be minimized. When the quantity of sales can be predicted accurately, the fulfillment of consumer demand can be managed in a timely manner and the company's cooperation with consumers is maintained properly so that the company can avoid losing sales and consumers. This study aims to analyze the accuracy of predicting the quantity of sales of salt using the Single Exponential Smoothing (SES) method compared to using the Double Exponential Smoothing (DES) method, so that a more accurate method will be obtained for predicting the quantity of sales. The results of testing the comparison of the level of accuracy can be done by evaluating the error value of the forecasting results with the Mean Absolute Percentage Error (MAPE). The lowest MAPE result obtained is in the SES method when the parameter α = 0.054 with a MAPE result of 7.932% which means the accuracy value is very accurate. Whereas with the DES method the MAPE value is 28.145% while the parameter α = 0.845 β = 0.214 which means the value of accuracy is reasonable. Based on the MAPE results obtained using the two methods above, the Single Exponential Smoothing method is more accurate for use in predicting salt sales. Whereas with the DES method the MAPE value is 28.145% while the parameter α = 0.845 β = 0.214 which means the value of accuracy is reasonable. Based on the MAPE results obtained using the two methods above, the Single Exponential Smoothing method is more accurate for use in predicting salt sales. Whereas with the DES method the MAPE value is 28.145% while the parameter α = 0.845 β = 0.214 which means the value of accuracy is reasonable. Based on the MAPE results obtained using the two methods above, the Single Exponential Smoothing method is more accurate for use in predicting salt sales
IMPLEMENTATION OF DATA MINING MODELS WITH ALGORITHMS K-NEAREST NEIGHBOR IN MONITORING THE NUTRITIONAL STATUS OF CHILDREN AND STUNTING -, mutammimul; Fachrurrazi, Sayed; Rizal, Reyhan Achmad; -, Mauliza; -, Syarkawi
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.v6i2.3376

Abstract

Information systems are needed in the development of children in the developmental period and especially in the world of health. Monitoring of children's nutritional status and stunting is necessary to determine children's weight and meet the criteria for children's nutritional status. Pukesmas Muara Satu, North Aceh District, is an implementing element or assistant to the duties of Poskesdes and Midwives in the Health of children's nutritional status and stunting in Paloh Punti Village, which is one of the agencies under the Ministry of Health. This study aims to monitor the growth and development of children such as measuring weight, height, measured to detect early if unwanted things occur such as malnutrition. The problem in this study is designing and monitoring an Information system for child nutritional status and stunting that is integrated with a web application. The purpose of this study is to find out staff and employees in managing, monitoring and accessing data. So that the data at the puskesmas is recorded in the system, and can quickly determine data on the nutritional status of children and stunting. The results of this study are to be able to find out an information system that is able to reduce problems that occur in managing data on the nutritional status of children and stunting at the Muara Satu Health Center. This system is very important because it can make it easier for staff to record the nutritional status of children and stunting at the Health Center. then the results of the KNN (K-Nearest Neighbor) model classification with the recapitulation of the value of new cases with old cases in the first test section is 0.6944, the second test is 0.6388, the third test is 0.555, the fifth test is 0.6388.
Co-Authors -, Mauliza -, mutammimul -, Syarkawi Aisyah - Andy Chandra Asmaul Husna Bintang, Lidya Haryati Delima Sitanggang, Delima Ericky Benna Perolihin Manurung Giovano, Steven Girsang, Imron Sanjaya Harly, Jesslyn HS, Christnatalis Jonathan, Jovi Manik, Brian Fernanda Manurung, Ericky Benna Perolihin Mutammimul Ula Nababan, Marlince Prasetiyo, Sidik Apriyadi Sandy, Cut Lika Mestika Sayed Fachrurrazi Sembiring, Lysheeba Abbygail Simanjuntak, Ester Farida Sinaga, Jasmin William Natanael Sukiman, T. Sukma Achriadi Susanto, Mario Syahputra, Dinur Tampubolon, Tasya Rouli Christy Ulva , Ananda Faridhatul Yennimar, Yennimar Zulfikri, Abdi