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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer SEMIRATA 2015 Seminar Nasional Informatika (SEMNASIF) CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Teknik Informatika UNIKA Santo Thomas MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Query : Jurnal Sistem Informasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH KOMPUTA : Jurnal Ilmiah Komputer dan Informatika CSRID (Computer Science Research and Its Development Journal) Jurnal Varian JTIK (Jurnal Teknik Informatika Kaputama) KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JUKI : Jurnal Komputer dan Informatika MEANS (Media Informasi Analisa dan Sistem) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Ipteks Terapan : research of applied science and education Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Dinamika Informatika (JDI) Data Sciences Indonesia (DSI) International Journal of Economic, Technology and Social Sciences (Injects) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Proceeding Of International Conference On Education, Society And Humanity Jurnal Teknik Indonesia (JU-TI)
Zakarias Situmorang
FIKOM, Universitas Katolik Santo Thomas
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PROTOTIPE SISTEM FIRE DETECTOR BERBASIS ARDUINO UNO DAN WEB Rubianto Rubianto; Zakarias Situmorang; Yusfrizal Yusfrizal
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 6 No. 2 (2022): Volume 6, Nomor 2, Juli 2022
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v6i2.213

Abstract

Banyaknya bencana kebakaran yang dapat muncul dari hal-hal yang sepele yang dapat menimbulkan kerugian berupa materil, psikologis bahkan korban jiwa. Prototipe ini dirancang untuk dapat mendeteksi kebakaran pada suatu ruangan, yang terdiri dari Sensor Suhu LM35DZ, Sensor Gas MQ-135, Mikrokontroler Arduino Uno, Modul Ethernet Shield W5100 dan adaptor sebagai catu daya. Kemudian, sensor suhu akan mendeteksi suhu di sekitar ruangan (dalam satuan ⁰C) dan sensor gas akan mendeteksi gas yang ada di sekitar ruangan, khusunya karbon monoksida (dalam satuan ppm). Ketika kedua sensor mendeteksi suhu dan gas dalam jumlah tertentu, maka akan dibandingkan dengan setpoin yang sudah di tetapkan pada mikrokontroler Arduino Uno. Jika hasil pengukuran kedua sensor lebih kecil dari setpoin, maka tidak ada tampilan peringatan. Tetapi jika hasil pengukuran kedua sensor lebih besar dari setpoint, maka akan muncul peringatan. Peringatan akan ditampilkan dalam web menggunakan bantuan Modul Ethernet Shield W5100 melalui koneksi LAN. Tampilan pada web, berupa peringatan “Bahaya”. Jadi ketika hasil pengukuran lebih besar dari setpoin, dapat dimungkinkan terjadi kebakaran pada ruangan tersebut. Prototipe ini dapat bekerja baik di dalam ruangan. Selisih perbandingan suhu antara LM35 dengan termometer batang adalah 0,18 ⁰C.
Transfer Learning for Feral Cat Classification Using Logistic Regression Fazli Nugraha Tambunan; Rika Rosnelly; Zakarias Situmorang
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.27

Abstract

Machine learning is an alternative tool for classifying animal species, especially feral cats. In this research, we use a machine learning algorithm to classify three species of feral cats: American Wildcat, Black-footed Cat, and European Wildcat. We also use a transfer learning model using the VGG-19 network for extracting the features in the feral cat images. By combining the VGG-19 and logistic regression algorithm, we build six models and compare which one is the best to solve the problem. We evaluate and analyze all models using a 5-fold, 10-fold, and 20-fold cross-validation, with accuracy, precision, and recall as the base performance value. The best result obtained is a model with a lasso regularization and cost parameter value of 1, with an accuracy value of 0.846667, a precision value of 0.845389, and a recall value of 0.846667. We also tune the C parameter in each LR model with values such as 0.1, 0.5, and 1. The most optimum C value for the lasso and ridge regularization is one, resulting in an average value of accuracy = 0.813, precision = 0.812, and recall = 0.813.
Bulldog Breed Classification Using VGG-19 and Ensemble Learning Abwabul Jinan; Zakarias Situmorang; Rika Rosnelly
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.29

Abstract

In image classification, the C4.5, Adaboost, and Gradient Boosting algorithms need another method to extract the image's features in the classification process. This research employs transfer learning with the VGG-19 network for the image's features extraction and transfers the result as a dataset to classify image-based Bulldog breeds. As the classifier to classify the extracted features from the VGG 16 model, we employ three ensemble learning algorithms, namely C4.5, AdaBoost, and Gradient Boost. The training data classification results of the American, English, and French bulldog breeds show that, with a 20-fold cross-validation evaluation, the Gradient Boosting algorithm performs the best, with an accuracy value of 0.958, a precision value of 0.958 and recall value of 0.933. And show the highest accuracy (0.933), precision (0.938), and recall (0.933) in the testing data classification. While in the testing data classification, the Gradient Boosting algorithm scores an accuracy value of 0.933, a precision value of 0.938, and a recall value of 0.933
Analysis of Machine Learning Algorithms in Predicting the Flood Status of Jakarta City Irwan Daniel; Hartono Hartono; Zakarias Situmorang
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.38

Abstract

By mining the information in the dataset, we can solve a prediction problem, especially flood status prediction based on floodgate levels, using machine learning algorithms. This research employs three machine learning algorithms (K-Nearest Neighbor, Naive Bayes, and Support Vector Machine) for predicting the flood status using a dataset containing the data of DKI Jakarta's floodgate levels. Using a 5-fold, 10-fold, and 20-fold cross-validation evaluation, we get the highest accuracy (85.096%), f-score (85.1%), precision (85.641%), and recall (85.096%) from the model using the SVM algorithm with a polynomial kernel. Average performance-wise, the K-NN algorithm performs better than the other algorithm with an average accuracy of 83.147%, an average f-score of 83.156%, an average precision of 83.566%, and an average recall of 83.147%
Comparative Analysis of Support Vector Machine And Perceptron Algorithms In Classification Of The Best Work Programs In P2KBP3A Jaka Tirta Samudra; Rika Rosnelly; Zakarias Situmorang
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.40

Abstract

With the rapid growth of government agencies that are required to carry out an activity in every aspect that publishes and carries out obligations every year, it is required to be held accountable and also implemented for every device that receives such as fostered villages by utilizing the available APBD funds to maximize the work program that has been designed. so that it can be implemented as much as possible. That way, to get the best from all aspects of every work program implementation, there must be an important point from the annual work program design that is made without exception. Data mining itself can help P2KBP3A in analyzing each work program that is designed before being implemented in the future for the annual work program by looking at various aspects of past work program data and grouping work programs in the form of classification. In designing the work program, this research builds a classification model by adding a sigmoid activation function that uses SVM and perceptron to compare the accuracy results of the algorithm used to get the best work program design. From the various classifications used, the best value for classifying the dataset of the best P2KBP3A work programs can be seen from the average accuracy value of 87.5%, F1 value of 82.2%, the precision value of 80.2%, and recall value of 87.5%
Artificial Neural Network Backpropagation Method to Predict Tuberculosis Cases Lestari, Valencya; Mawengkang, Herman; Situmorang, Zakarias
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11998

Abstract

Artificial neural networks are information processing systems that have certain performance characteristics in common with biological neural networks. Backpropagation is a method in artificial neural networks that uses supervised learning. Backpropagation has a weakness in reaching the convergence level. The convergence rate is the difference from the mean square error value. The mean square error is the difference between the target value and the actual value. One way to increase the convergence rate is to provide input values. in this study using the nguyen widrow backpropagation method. The network will predict Tuberculosis cases. Data sourced from the North Sumatra Provincial Health Office from 2019 to 2021. architectural testing with a learning rate ranging from -0.5 to 0.5 and momentum ranging from 0 to 1 obtained a learning rate of 0.5, the epoch process stops at the 172nd iteration with an achievement gradient of 0.0001598 and the R value for training data is 0.99841 which means it is very good because it is close to 1 with an accuracy rate of 81.82%.  
Multilayer Perceptron Performance Analysis in Liver Disease Classification Pradipta, Muhammad Iqbal; Situmorang, Zakarias; Sembiring, Rahmat Widya
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13202

Abstract

Liver disease is a liver disease caused by viruses, alcohol, lifestyle and others. Someone often does not realize or is late to know liver disease so that when examined liver disease is severe, it would be better if treatment is done early by knowing the symptoms suffered. Data mining can help diagnose liver disease more easily, especially to help doctors determine whether patients suffer from liver disease or not, with symptoms almost close to liver disease. The process of diagnosing liver disease is carried out by a classification process and the result is that the patient suffers from liver or not. This research uses a data mining classification method using an artificial neural network method, namely Multilayer Perceptron. The Indian Liver Patient Dataset (ILPD) used in this study was obtained from the UCI Machine Learning Repository. The division of the data set over the training data and test data is done by Cross Validation. Performance measurement of the method uses confusion matrix. Based on the research conducted, it was found that the application of Multilayer Perceptron resulted in varying accuracy based on testing with different Fold values with the highest accuracy of 83.70% when the Fold was 7, and the lowest accuracy of 80.57% when the Fold was 3. Then the average accuracy of all Fold tests is 82.13%
Analysis of The Use of Nguyen Widrow Algorithm in Backpropagation in Kidney Disease Damanik, Romanus; Zarlis, Muhammad; Situmorang, Zakarias
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13608

Abstract

Fast and accurate diagnosis is very important for kidney disease. This research conducts and analyzes by using Nguyen Widrow Algorithm in Back Propagation method in artificial neural network for kidney disease diagnosis with the aim to improve the accuracy in predicting and time efficiency in diagnosing. The Nguyen Widrow algorithm is very capable of accelerating convergence and stabilizing the learning process in artificial neural networks, which is also expected to present a meaningful contribution to the handling of health data. This study uses MATLAB as a platform for algorithm implementation and a dataset of medical records of kidney disease patients collected from a hospital that specializes in treating kidney disease patients. The data pre-processing and artificial neural network modeling stages use the Nguyen Widrow algorithm, while the model training process uses the Back Propagation method. The results showed that the Nguyen Widrow algorithm was able to improve the accuracy of predicting someone suffering from kidney disease compared to using only the Back Propagation method. Analysis of the performance of the model shows a significant improvement in stability and convergence speed during the learning process. This indicates that data processing and medical decision making becomes more efficient. On the other hand, this research also studied the challenges and limitations that will be faced in terms of implementation of the Nguyen Widrow algorithm. Also the sensitivity of the initialization parameters, the need for the quality of the dataset to be used in training the model.This research reveals the ability of the Nguyen Widrow algorithm to improve the performance of artificial neural networks in diagnosing kidney disease. By implementing this algorithm in MATLAB, the results show that the use of the latest data processing technology and analysis tools can provide significant improvements in accuracy and efficiency in the medical field. In addition, this research is expected to provide a new direction in the development of machine learning algorithms for applications in the healthcare field, especially for diagnosing kidney disease. By further utilizing this technology, it contributes significantly to improving the quality of healthcare and treatment outcomes for patients suffering from kidney disease.
Data Science bidang Pemasaran : Analisis Prilaku Pelanggan Harahap, Mawaddah; Lubis, Yusniar; Situmorang, Zakarias
Data Sciences Indonesia (DSI) Vol. 1 No. 1 (2021): Article Research Volume 1 Issue 1, June 2021
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v1i1.1194

Abstract

Dalam kegiatan pemasaran digital, data Science (DS) memiliki peran penting dalam memahami kinerja industri pemasaran sebelum menerapkan teknik pemasaran digital pada pemasaran produk. Hal ini dikarenakan setiap pelanggan merespons secara berbeda setiap penawaran. Perilaku pelanggan juga berubah berdasarkan waktu karena mereka mungkin memiliki kebutuhan yang berbeda pada situasi yang berbeda. Pada makalah ini fokus menyajikan analisis bisnis dengan penerapan DS untuk mengeksplorasi pola perilaku dan juga memprediksi bagaimana pelanggan akan merespons penawaran yang berbeda. Penerapan analisis data eksplorasi juga diterapkan untuk menjawab beberapa pertanyaan bisnis, dari hasil pengamatan menghasilkan lima kelompok pelanggan yang disajikan dalam bentuk visualisasi dan model Random Forest Classifier memiliki skor akurasi prediksi terbaik sebesar 91%, kemudian K neighbors Classifier dan Logistic Regression.
Analisis Sentimen Aplikasi Playstore Sirekap 2024 Pasca Pilpres Dengan Perbandingan Metode Support Vector Machine (SVM), Naïve Bayes Classifier Dan Random Forest. TARIGAN, Dede Ardian; Situmorang, Zakarias; Rosnelly, Rika
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 3: Juni 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025129608

Abstract

Aplikasi Sirekap merupakan sebuah aplikasi berbasis website yang mengandalkan teknologi Optical Character Recognition (OCR) dan Optical Mark Reader (OMR) dalam pengoperasiannya. Perkembangan teknologi ini digunakan untuk mempermudah proses perhitungan suara dengan QuickCount yang sifatnya sementara oleh KPU pada Pemilihan Presiden dan Wakil-Presiden Indonesia Periode 2024. Kedua teknologi tersebut memiliki peran penting dalam mengotomatisasi pola proses baca dan hitung secara real-time. Dengan demikian, analisis sentimen diperlukan untuk mengekstraksi komentar teks dari opini publik tentang aplikasi Sirekap 2024 di Play Store. Penelitian ini berkaitan dengan analisis sentimen terkait hasil perhitungan suara yang menimbulkan ketidaksesuaian di aplikasi Sirekap 2024, apakah bersifat positif atau negatif. Tahapan teknik yang digunakan dalam penelitian ini meliputi scraping data, pre-processing data, pelabelan pola, ekstraksi fitur/pembobotan, pembagian data, dan proses klasifikasi analisis sentimen. Pengumpulan data primer dilakukan menggunakan program Python di aplikasi Google Colab dengan teknik google play scraper di aplikasi playstore android Sirekap 2024. Metode klasifikasi yang digunakan adalah Support Vector Machine, Naïve Bayes, dan Random Forest untuk mengklasifikasikan data. Hasil klasifikasi SVM adalah 82%, Naïve Bayes adalah 71%, dan Random Forest adalah 81%. Dari ketiga metode klasifikasi, kinerja terbaik dalam mengidentifikasi adalah metode klasifikasi SVM dengan akurasi 82%, presisi 82%, recall 82%, dan F1-Score 82%.   Abstract The Sirekap application is a web-based application that relies on Optical Character Recognition (OCR) and Optical Mark Reader (OMR) technology in its operation. The development of this technology is used to facilitate the vote counting process with a temporary QuickCount by the KPU in the 2024 Indonesian Presidential and VicePresidential Elections. Both technologies play an important role in automating the reading and counting process patterns in real-time. Thus, sentiment analysis is necessary to extract text comments from public opinion about the Sirekap 2024 application on the Play Store. This research is related to sentiment analysis concerning the vote count results that cause discrepancies in the Sirekap 2024 application, whether they are positive or negative. The technical stages used in this research include data scraping, data pre-processing, pattern labeling, feature extraction/weighting, data splitting, and the sentiment analysis classification process. Primary data collection was conducted using a Python program in the Google Colab application with the Google Play scraper technique in the Sirekap 2024 Android Play Store application. The classification methods used are Support Vector Machine, Naïve Bayes, and Random Forest to classify the data. The SVM classification result is 82%, Naïve Bayes is 71%, and Random Forest is 81%. Among the three classification methods, the best performance in identification is the SVM classification method with an accuracy of 82%, precision of 82%, recall of 82%, and F1-Score of 82%.
Co-Authors A F Limas Ptr Abwabul Jinan Adam Sagara Ade Clinton Sitepu Ade Clinton Sitepu Adelina, Mimi Chintya Aditia Rangga Agus Fahmi Limas Putra Alex Rikki Sinaga Anirma Kandida Br Ginting Ashari, Annisa Asrizal Asrizal Asyahri Hadi Nasyuha B. Herawan Hayadi Budi K. Hutasuhut Daim Azhari Parinduri Desi Irfan Desinta Purba Devis Wisley Situmorang Doughlas Pardede Efendi, Syahril Ela Roza Batubara Emerson P Malau Erikson Putra Perdana Lumban Tobing Erna Budhiarti Nababan Fazli Nugraha Tambunan Ginting, Emnita Boru Handayani, Meli Hartono Hartono Herman Mawengkang Husein, Alice Erni Ichsan Firmansyah Irwan Daniel Irwan Daniel Ita Juwita Saragih Jaka Tirta Samudra Jazi Eko Istiyanto Junaidi Junaidi Junerdi Nababan Karina Andriyani Kelvin Leonardi Kohsasih Khoirunsyah Dalimunthe Kusuma, Jaka Lestari, Valencya Lewis, Andreas Lubis, Cindy Paramitha Lusi Herlina Siagian M Anggi Rivai Nst Manungkalit, Jupri Margolang, Khairul Fadhli Masri Wahyuni Mawaddah Harahap Mawaddah Harahap, Mawaddah Melda Pita Uli Sitompul Mora Helena Tambunan Muhadi M. Ilyas Gultom Muhammad Mizan Siregar Muhammad Zarlis Muhammad Zarlis, Muhammad Opim Salim Sitompul P.P.P.A.N.W. Fikrul Ilmi R.H. Zer Pradipta, Muhammad Iqbal Pratiwi, Mariska Putri Puji Sari Ramadhan Purba, Andry Hery Putrama Alkhairi Raden Aris Sugianto Rahmad, Sofyan Retantyo Wardoyo Riandini, Maisarah Ridha Maya Faza Lubis Ridha Maya Faza Lubis Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly, Rika Rimbun Siringoringo Romanus Damanik Roslina, Roslina Rubianto Rubianto Sagala, Masdiana Sartika Mandasari Sembiring, Rahmat Widya Siahaan , Marulak Lasron Sinaga, Novendra Adisaputra Sipayung, Sardo Pardingotan Sitorus, Lamhot Sri Hartati Sugeng Riyadi Tarigan, Dede Ardian Tulus Tulus Wanayumini Yoppi, Edunal Yuni Franciska Yusfrizal Yusfrizal Yusniar Lubis Yusniar Lubis Yusniar Lubis Zekson Matondang