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All Journal Bulletin of Electrical Engineering and Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Journal of Computer Networks, Architecture and High Performance Computing Yayasan Cita Cendikiawan Al Khwarizmi Data Sciences Indonesia (DSI)
Mawaddah Harahap, Mawaddah
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Penerapan Metode Distance Transform Pada Kernel Discriminant Analysis Untuk Pengenalan Pola Tulisan Tangan Angka Berbasis Principal Component Analysis: Penerapan Metode Distance Transform Pada Kernel Discriminant Analysis Untuk Pengenalan Pola Tulisan Tangan Angka Berbasis Principal Component Analysis Husein, Amir Mahmud; Harahap, Mawaddah
Sinkron : jurnal dan penelitian teknik informatika Vol. 2 No. 1 (2017): SinkrOn Volume 2 Nomor 1 Oktober 2017
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (713.75 KB)

Abstract

Pengenalan pola merupakan salah satu bidang penelitian yang cukup popular Karena dapat digunakan untuk berbagai keperluan. Penelitian ini bertujuan membangun sebuah aplikasi untuk dapat mengenali sebuah objek tulisan tangan angka secara langsung dengan penerapan metode Distance Transform (DT) Pada Algoritma Kernel Discriminant Analysis (KDA) Berbasis Principal Component Analysis (PCA). Penerapan PCA untuk proses segementasi sedangkan KDA untuk ekstrasi fitur pola tulisan tangan angka, DTdiusulkan untuk memperbaiki performa KDA terhadap waktu komputasi dengan PCA untuk ekstraksi. Kerangkan analisis yang diusulkan menggunakan dua pendekatan, pendekatan pertama analisa kinerja PCA+KDA, kemudian PCA+KDA dengan DT, kedua hasil pendekatan akan dibandingkan untuk mengetahui dampak DT terhadap KDA berbasis PCA pada pengenalan pola tulisan tangan angka secara langsung.Berdasarkan hasil pengujian metode DT yang diusulkantidak berpengaruh secara signifikan untukmemperbaiki kelemahan KDA pada optimasi waktu, namun untuk ekstraksi pada kernelyang berbeda dengan tingkat akurasi pengenalantulisan tangan angka secara langsung 95,5% dibandingkan kombinasi KDA berbasis PCA sebesar 87,98%
Monitoring patient health based on medical records using fuzzy logic method Harahap, Mawaddah; Pratama, Ari Rizki; Hutagalung, Delano Ariesagita; Siregar, Wali; Sihombing, Hendra
Sinkron : jurnal dan penelitian teknik informatika Vol. 3 No. 2 (2019): SinkrOn Volume 3 Number 2, April 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (512.023 KB) | DOI: 10.33395/sinkron.v3i2.10014

Abstract

The Fuzzy Logic concept was first introduced by Prof. Lotfi Zadeh from the University of California at Berkeley in 1965, and presented not as a control methodology, but as a way of processing data by allowing the use of partial set membership compared to the crisp set membership or non-membership. Along with the development of computer technology, the concept of fuzzy logic is increasingly needed by people, because this concept is able to provide information needed in the decision making process. This study aims to analyze and design intelligent systems to monitor the health development of inpatients. The method used is the Fuzzy Logic method. This method will predict the level (degree) of health of each patient based on the amount of drug use and the durationof diagnosis process. The tools used to analyze and design the system are Unified Modeling Language. The results of this research are monitoring the health development of patients using the Fuzzy Logic method.
Application for Employee Performance Assessment Using Profile Matching Method Aisyah, Siti; Purba, Windania; Harahap, Mawaddah; 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 (444.87 KB) | DOI: 10.33395/sinkron.v4i1.10225

Abstract

Human resources an important role for the agency. Good employee performance can provide a good image for the company. Many companies give rewards or rewards to their employees for their work performance. The assessment is done in addition to giving or appreciation as well as motivation for employees to work better. Problems that often occur in the employee appraisal process are the large number of employees and the criteria assessed and data processing are still in conventional process so that takes a long time and the results of the assessment are still not objective. To overcome, application was built could simplify the employee performance appraisal process. The method used is Profile Matching to assess and determine employees who excel. Factors or criteria used in the form of performance, discipline, honesty, years of service, cooperation. Profile matching is broadly the process of comparing the actual data value of a profile to be assessed with the expected profile value. Application is built based on web with PHP programming and MYSQL. To help the process of employee performance appraisal at Universitas Prima Indonesia. Collecting data in research using literature studies, observations, interviews, and sampling. Result the research is Profile Matching Method can be use for Decision Support System in determining employee achievement, with the highest calculation results in the sample data obtained by A3 and the lowest position obtained by A1. In academic, research can be an enrichment of teaching materials especially in subject of Decision Support Systems and information systems in general.
IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK DALAM KLASIFIKASI SEL DARAH MERAH YANG TERPENGARUH MALARIA Harahap, Mawaddah; Jefferson, Jefferson; Barti, Surya; Samosir, Suprianto; Turnip, Christi Andika
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i2.10713

Abstract

Malaria is a disease caused by plasmodium which attacks red blood cells. Diagnosis of malaria can be made by examining the patient's red blood cells using a microscope. Convolutional Neural Network (CNN) is a deep learning method that is growing rapidly. CNN is often used in image classification. The CNN process usually requires considerable resources. This is one of the weaknesses of CNN. In this study, the CNN architecture used in the classification of red blood cell images is LeNet-5 and DRNet. The data used is a segmented image of red blood cells and is secondary data. Before conducting the data training, data pre-processing and data augmentation from the dataset was carried out. The number of layers of the LeNet-5 and DRNet models were 4 and 7. The test accuracy of the LeNet-5 and DrNet models was 95% and 97.3%, respectively. From the test results, it was found that the LeNet-5 model was more suitable in terms of red blood cell classification. By using the LeNet-5 architecture, the resources used to perform classification can be reduced compared to previous studies where the accuracy obtained is also the same because the number of layers is less, which is only 4 layers
Grape disease detection using dual channel Convolution Neural Network method Harahap, Mawaddah; Angelina, Valencia; Juliani, Fenny; Celvin; Evander, Oscar
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i2.10939

Abstract

Grapes are one type of fruit that is usually used to make grape juice, jelly, grapes, grape seed oil and raisins, or to be eaten directly. So far, checking for disease in grapes is still done manually, by checking the leaves of the grapes by experts. This method certainly takes a long time considering the extent of the vineyards that must be evaluated. To solve this problem, it is necessary to apply a method of detecting grape disease, so that it can help the common people to detect grape disease. This research will use the Dual-Channel Convolutional Neural Network method. The process of detecting grape disease using the DCCNN method will begin with the extraction of the leaves from the input image using the Gabor Filter method. After that, the Segmentation Based Fractal Co-Occurrence Texture Analysis method will be used to extract the features, color, and texture of the extracted leaves. The result is the number of datasets will affect the accuracy of the results of disease identification using the DCCNN method. However, more datasets will cause the execution process to take longer. Changes in the angle and frequency values in the Gabor method at the time of testing will reduce the accuracy of the test results. The conclusion of this study are the DCCNN method can be used to detect the type of leaf disease in grapes and the number of datasets will affect the accuracy of the results of disease identification using the DCCNN method.
Identification of Face Mask With YOLOv4 Based on Outdoor Video Harahap, Mawaddah; Kusuma, Leonardo; Suryani, Melva; Situmeang, Candra Ebenezer; Purba, Juniven Francisco
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11190

Abstract

The use of face masks in the current era is one of the special regulations in many countries including Indonesia to prevent the spread of coronavirus. However, not all people strongly agree to wear masks because they feel uncomfortable to wear even in crowded places require the use of masks such as shopping malls, hospitals, factories, stations and others by checking manually. Therefore, in the study proposed automatic detection of masks with YOLOv4 with the stage of data collection recording community activities in crowded places, labeling images of masks and non masks. The labelling results were conducted in training that resulted in 90.3% accuracy in the 2000 ierasi, the last of which was video testing in three different crowd locations: taxes, city parks and highways. Based on the test results, YOLOv4 can detect masks and non masks on videos with different obstruction conditions such as people wearing helmets, hand obstacles. However, for the detection of people with tissue obstruction conditions and improper position of wearing masks has not resulted in good detection.
Clustering Algorithm For Determining Marketing Targets Based Customer Purchase Patterns And Behaviors Husein , Amir Mahmud; waruwu , Februari Kurnia; Batu Bara , Yacobus M.T.; Donpril, Meleyaki; Harahap, Mawaddah
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11191

Abstract

Customer segmentation is one of the most important applications in the business world, specifically for marketing analysis, but since the Corona Virus (Covid-19) spread in Indonesia it has had a significant impact on the level of digital shopping activities because people prefer to buy their needs online, so It is very important to predict customer behavior in marketing strategy. In this study, the K-Means Clustering technique is proposed on the RFM (Recency, Frequency, Monetary) model for segmenting potential customers. The proposed model starts from the data cleaning stage, exploratory analysis to understand the data and finally applies K-Means Clustering to the RFM Model which produces three clusters based on the Elbow model. In cluster 0 there are 2,436 customers, in cluster1 1,880 and finally in cluster2 there are 18 customers. RFM analysis can segment customers into homogeneous groups quickly with a minimum set of variables. Good analysis can increase the effectiveness and efficiency of marketing plans, thereby increasing profitability with minimum costs.
PERBANDINGAN KINERJA ALGORITMA RANDOM FOREST CLASSIFIER DAN LIGHTGBM CLASSIFIER UNTUK PREDIKSI PENYAKIT JANTUNG Duran, Filbert; Wijaya, Frederico; Hulu, Yakin Rianto; Harahap, Mawaddah; Prabowo, Agung
Data Sciences Indonesia (DSI) Vol. 3 No. 2 (2023): Article Research Volume 3 Issue 2, December 2023
Publisher : ITScience (Information Technology and Science)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v3i2.3831

Abstract

Penyakit jantung merupakan masalah kesehatan serius yang dapat dicegah dan diobati. Dengan menjaga gaya hidup sehat, melakukan pemeriksaan kesehatan secara rutin, dan mengikuti anjuran dokter[1], risiko penyakit jantung dapat dikurangi. Random Forest Classifier (RFC) bagaikan hutan pohon keputusan yang bekerja sama untuk menghasilkan prediksi yang lebih jitu. Algoritma ini tergolong handal dan fleksibel, mampu menangani berbagai tugas klasifikasi dan regresi. Kelebihannya, RFC menawarkan akurasi tinggi, tahan terhadap overfitting, dan mudah diinterpretasikan[2]. RFC adalah algoritma machine learning yang kuat dengan banyak keunggulan, namun perlu dipertimbangkan pula keterbatasannya dalam hal komputasi dan fleksibilitas[3]. LightGBM merupakan algoritma machine learning yang kuat dan efisien untuk klasifikasi dan regresi. Kecepatan, akurasi, dan kemudahan penggunaannya menjadikannya pilihan yang menarik untuk berbagai aplikasi[4]. Dari hasil yang didapat dari penelitian ini adalah metode RFC dan LightGBM dapat disimpulkan bahwa metode RFC merupakan metode yang tergolong efektif dalam analisis penyakit jantung dengan akurasi prediksi dari model adalah 95,37%., dapat dikatakan bahwa metode Random Florest Classifier cocok untuk melakukan analisis penyakit jantung bedasarkan dataset yang ada.
Klasifikasi Buah Guava Menggunakan Computer Vision Zizwan Putra, Adya; Harahap, Mawaddah; Husein, Amir; Simarmata, Allwin
Data Sciences Indonesia (DSI) Vol. 3 No. 2 (2023): Article Research Volume 3 Issue 2, December 2023
Publisher : ITScience (Information Technology and Science)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v3i2.4006

Abstract

Penelitian ini bertujuan untuk mengembangkan sebuah sistem klasifikasi buah guava menggunakan teknologi computer vision. Klasifikasi buah guava yang akurat dan otomatis dapat membantu dalam proses identifikasi buah guava yang baik kualitasnya dan dapat digunakan dalam industri pertanian, perdagangan buah-buahan, serta penelitian lanjutan. Metode yang digunakan dalam penelitian ini melibatkan beberapa langkah. Pertama, dilakukan pengumpulan data citra buah guava yang meliputi variasi jenis guava yang berbeda serta berbagai kondisi pencahayaan dan latar belakang. Data citra tersebut kemudian diolah dan dipreproses untuk mengurangi derau dan meningkatkan kualitas citra. Setelah proses ekstraksi fitur selesai, dilakukan pelatihan model klasifikasi menggunakan data citra buah guava yang telah diklasifikasikan secara manual oleh ahli. Model klasifikasi yang terlatih kemudian diuji menggunakan data citra buah guava yang belum pernah dilihat sebelumnya untuk mengukur tingkat akurasi dan performa sistem. Hasil penelitian ini diharapkan dapat menghasilkan sistem klasifikasi buah guava yang akurat dan dapat diandalkan. Dengan menggunakan teknologi computer vision, proses identifikasi buah guava dapat dilakukan secara cepat dan otomatis. Keberhasilan penelitian ini dapat memberikan kontribusi dalam meningkatkan efisiensi industri pertanian dan perdagangan buah-buahan, serta memberikan landasan bagi penelitian lebih lanjut dalam bidang pengolahan citra dan pengenalan pola.
IMPLEMENTASI METODE TSUKAMOTO PADA ANALISIS PREDIKSI HASIL KELAPA SAWIT Harahap, Mawaddah; Nababan, Siska Yanti
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 3 No. 1 (2020): Jutikomp Volume 3 Nomor 1 April 2020
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v3i1.458

Abstract

Tanaman kelapa sawit merupakan tanaman yang banyak dikebunkan oleh perusahaan-perusahaan yang besar, baik pemerintahan maupun swasta. Perkebunan sawit merupakan salah satu sektor perkebunan unggul yang mengalami perkembangan yang cukup pesat. Dari perkebunan dapat dihasilkan komuditi ekspor terbesar setelah subsektor pertambangan minyak dan gas serta kehutanan. Oleh karena itu tujuan dari pengembangan aplikasi ini yaitu untuk menghasilkan prediksi hasil kelapa sawit dari metode Fuzzy Inference System Tsukamoto. Logika Fuzzy adalah salah satu cabang ilmu kecerdasan buatan untuk membangun system cerdas. Logika Fuzzy sering digunakan dalam pemecahan masalah yang menjelaskan system bukan melalui angka-angka, melainkan secara linguistik atau variabel-variabel yang mengandung ketidakpastian/ketidaktegasan. Penelitian ini menggunakan Fuzzy Inference System Tsukamoto untuk memprediksi hasil kelapa sawit. Setelah melakukan pengujian menggunakan metode tsukamoto maka mendapatkan hasil akurasi tertinggi 97,58% dan akurasi terendah adalah 11,72%.
Co-Authors Achmad Nurhadi Agung Prabowo Amir Husein Amir Mahmud Husein, Amir Mahmud Amir Saleh Andro Eriel Tambun Angelina, Valencia Barti, Surya Batu Bara , Yacobus M.T. Celvin Chuanta, Roy Vidia Ciptady, Kalvintirta Damanik, Jansen Liharma Donpril, Meleyaki Duran, Filbert Evander, Oscar Fernandito, Peter Ginting, Deskianta Ginting, Kenjiro Christian Hadyanto, David Hendra Sihombing Hidayati, Namira Hulu, Yakin Rianto Husein , Amir Mahmud Hutagalung, Delano Ariesagita Ibadurrahman Ibadurrahman Indra, Evta Jefferson, Jefferson Jonvin, Jonvin Juliani, Fenny Kinoto, Jovan Kusuma, Leonardo Kuswulandari, Sri Kwok, Shane Christian Leonardi, Jocelyn Lubis, Abdul Rahman Malau, Johannes Rianto Marcel, Rico Milatrisna, Dwi Yunita Nababan, Siska Yanti Ndruru, Yonata Ong, Derrick Kenji Panjaitan, Sumiati Pasaribu, Alfeus P. S. Pasaribu, Samuel Henock Hasangapon Pratama, Ari Rizki Purba, Juniven Francisco Purba, Windania Putra, Adya Putra, Adya Zizwan Rozi, Fachrul Samosir, Suprianto Samuel Samuel Sembiring, Giovan Sihombing, Josua Parulian Sihombing, Juniati Silitonga, Benny Art Simangungsong, Tentus Natoka Simarmata, Allwin Sinaga, Dedy Ridoly Sipangkar, Romulus Siregar, Josua Siregar, Saut Dohot Siregar, Wali Siti Aisyah Situmeang, Candra Ebenezer Situmorang, Erwin Tri Saputra Suryani, Melva Syahmir Defha Tambunan, Enjelyna Turnip, Christi Andika waruwu , Februari Kurnia Wijaya, Adrian Christian Wijaya, Frederico William William Winata, Davin Wizley, Vincent Yennimar, Yennimar Yusniar Lubis Zakarias Situmorang Zizwan Putra, Adya