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All Journal Dinamik Semantik Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) ANDHARUPA CESS (Journal of Computer Engineering, System and Science) Journal of Information System Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer Community: Jurnal Pengabdian Pada Masyarakat Jurnal Rekayasa elektrika Prosiding SeNTIK STI&K
Junta Zeniarja
Departemen Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Indonesia
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Physics Social Sciences Other

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Klasifikasi Penentuan skema Uji Sertifikasi di LSP UDINUS bagi mahasiswa Progrdi Sistem Informasi UDINUS dengan Algoritma Decision Tree (C4.5) Winarno, Agus; Warsito, Budi; Wibowo, Adi; Zeniarja, Junta
JOINS (Journal of Information System) Vol. 7 No. 2 (2022): Edisi November 2022
Publisher : Program Studi Sistem Informasi, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v7i2.6429

Abstract

AbstrakPenerapan Algoritma Decision Tree (C4.5) dan Eksperimen proses klasifikasi dilakukan menggunakan data nilai mahasiswa program studi S1 Universitas Dian Nuswantoro dengan 120.232 dataset menggunakan metode klasifikasi dengan Algoritma Decision Tree menghasilkan 99,99 % dan menghasilkan 7 klasifikasi  dengan rekomendasi ntuk pelatihan dan uji sertifikasi di Lembaga sertifikasi profesi UDINUS pada  5 klasifikasi yaitu klasifikasi  pred C sejumlah 2.355 data, pred BC sejumlah 4.633, pred B sejumlah 38.420 , pred AB sejumlah  33.414  dan pred A  sejumlah 37.230 dan 2  klasifikasi  yang tidak direkomendasikan yaitu klasifikasi pred D 1.440 dan pred E sejumlah 2.784 yang memberikan penyesuaian kurikulum pendidikan di Universitas Dian Nuswantoro dengan lembaga Sertifikasi dan Kebutuhan pekerjan. Kata Kunci: Sertifikasi, klasifikasi, mahasiswa, Tree Algorithm, rekomendasi
Machine Learning-Enhanced Geographically Weighted Regression for Spatial Evaluation of Human Development Index across Western Indonesia Firmansyah, Gustian Angga; Zeniarja, Junta; Azies, Harun Al; winarno, Sri; Ganiswari, Syuhra Putri
Journal of Applied Geospatial Information Vol 7 No 2 (2023): Journal of Applied Geospatial Information (JAGI)
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jagi.v7i2.6755

Abstract

The HDI (Human Development Index) is one of the important components to measure the level of success in efforts to improve the quality of human life. The human development index is built with three dimensions, namely the longevity and health dimension, the knowledge dimension and the decent standard of living dimension. The longevity and health dimension is measured using Life expectancy at birth. The knowledge dimension is measured using expected years of schooling and average years of schooling. Meanwhile, the decent standard of living dimension is measured using Adjusted per capita expenditure. This study aims to find factors that influence HDI (Human Development Index) in Western Indonesia Region using machine learning models. The results obtained are that HDI is influenced by average years of schooling, expected years of schooling, Life expectancy at birth, and Adjusted per capita expenditure which are sorted from the most significantly influential. The model used in this study is GWR (Geographically Weighted Regression) with evaluation results including, AIC of 215.3162, AICc of 226.5107, and the accuracy level in the form of R-square of 99.38% which means this model is good to use.
Simple Additive Weight Algorithm to Determine Lecturer Competency in Hybrid Learning Approach Pratama, Rifky Ariya; Winarno, Sri; Zeniarja, Junta
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 19, No 1 (2024): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v19i1.14158

Abstract

As the COVID-19 vaccination process continues, the pandemic is starting to subside. All educational institutes in Indonesia are starting to transition from online learning to hybrid learning. One crucial factor in the learning process is the competency of the lecturers. However, some students still feel dissatisfied with the learning process due to the lack of competence from the lecturers. This is exacerbated by the students and lecturers’ lack of familiarity with the hybrid learning system. Therefore, the aim of this research is to find a fair evaluation model for the lecturer’s competency that is suitable for the current hybrid learning approach. The data used in this research comes from questionnaires filled out by all students of Dian Nuswantoro University every year before the Final Semester Exam (UAS). The questionnaire consists of 10 questions regarding the hybrid learning process in the academic year of 2022/2023. Students provide their answers using a 4-point Likert scale, consisting of "Strongly Agree," "Agree," "Disagree," and "Strongly Disagree." The responses from students are grouped based on the courses/classes taught by the lecturers. The evaluation of lecturers’ competency is represented by two aspects: knowledge mastery and teaching skill. Each aspect of the lecturers’ evaluation consists of 5 questions in the questionnaire. The method used to evaluate the lecturers’ competency is the Decision Support System (DSS) algorithm combined with Simple Additive Weight (SAW). Result shows that students are mostly pleased with the quality of the lectures presented. Furthermore, lecturers with high evaluation scores tend to have a small number of students.
GridSearch and Data Splitting for Effectiveness Heart Disease Classification Putri, Rusyda Tsaniya Eka; Junta Zeniarja; Sri Winarno; Ailsa Nurina Cahyani; Ahmad Alaik Maulani
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.v9i1.13198

Abstract

Cardiovascular disease (CVD) is a major global health issue that affects death rates significantly. This research aims to improve the early detection and diagnosis of cardiovascular illness by utilizing machine learning methods, particularly classification algorithms. According to estimates from the World Health Organization (WHO), cardiovascular disease (CVD) caused 17.9 million deaths globally in 2019, or 32% of all fatalities. The treatment and prognosis of cardiovascular illness are greatly improved by early detection and diagnosis. Classification, in particular, machine learning, has become a prominent tool for solving problems connected to heart disease. The main objective of this project is to assess how well Grid Search and various data-sharing methods classify cardiac disease. SVM, Random Forest Classifier, Logistic Regression, Naïve Bayes, Decision Tree Classifier, KNN, and XGBoost Classifier are just a few machine learning methods. The UCI heart disease dataset, which contains information from 303 heart disease patients and 165 healthy participants, is used for the evaluation. Performance parameters like recall, accuracy, precision, and F1 score are considered to evaluate the algorithms' efficacy. The investigation's expected outcomes are intended to increase doctors' ability to diagnose cardiac disease more accurately. Moreover, these results may aid in creating more complex classification models for diagnosing cardiac conditions.
Comparison of Hyperparameter Optimization Techniques in Hybrid CNN-LSTM Model for Heart Disease Classification Maulani, Ahmad Alaik; Winarno, Sri; Zeniarja, Junta; Putri, Rusyda Tsaniya Eka; Cahyani, Ailsa Nurina
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.v9i1.13219

Abstract

Heart disease, which causes the highest number of deaths worldwide, recorded about 17.9 million cases in 2019, or about 32% of total global deaths, according to the World Health Organization (WHO). The significance of early detection of heart disease drives research to develop effective diagnosis systems utilizing machine learning. The advancement of machine learning in healthcare currently primarily serves as a supporting role in the ability of clinicians or analysts to fulfill their roles, identify healthcare trends, and develop disease prediction models. Meanwhile, deep learning has experienced rapid development and has become the most popular method in recent years, one of which is detecting diseases. The main objective of this research is to optimize the hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model for classifying heart disease by comparing hyperparameter optimization using grid search and random search. Although random search requires less time in hyperparameter tuning, the classification performance results of grid search show higher accuracy. In the test, the hybrid CNN and LSTM model with grid search achieved 91.67% accuracy, 89.66% recall (sensitivity), 93.55% specificity, 92.86% precision, 91.23% f1-score, and 0.9310 AUC value. These results confirm that using a hybrid CNN and LSTM model with a grid search approach is better suited for classifying heart disease.
Sentiment Analysis of Genshin Impact on X: Mental Health Implications Using TF-IDF and Support Vector Machine Jaya, Sava Irhab Atma; Junta Zeniarja
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13716

Abstract

Genshin Impact are now an integral part of daily life for many, potentially influencing mental well-being. Sentiment analysis window into these emotional effects, especially given the varied findings on gaming's impact on mental health. Analyzing X responses Genshin Impact using Support Vector Machine crucial, given its effectiveness in sentiment analysis. This study aims to deepen our understanding game's psychological impact and support development mental health interventions for gamers. The SVM classification report shows promising precision: 0.68 for Negative, 0.63 for Neutral, and 0.72 for Positive sentiment. However, recall rates favor Positive reviews (0.87) over Negative (0.56) and Neutral (0.51), reflected in the F1 score, highest for Positive sentiment at 0.79. With 174 Negative, 216 Neutral, and 333 Positive support counts, model achieved an overall accuracy of 0.69, effectively classifying Genshin Impact reviews based on sentiment. Analysis findings suggest a prevalence of positive opinions, indicating widespread player satisfaction with the game.
Peningkatan Performa Model Hard Voting Classifier dengan Teknik Oversampling ADASYN pada Penyakit Diabetes Anugrah, Muhammad Ikhsan; Zeniarja, Junta; Setiawan, Dicky Setiawan
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 1 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i1.25838

Abstract

Diabetes is a chronic disease that arises from excess sugar levels in the body and lack of exercise intensity resulting in a buildup in the blood. Indonesia ranks fifth as the country with the largest number of people with diabetes based on a report from the International Diabetes Federation (IDF). The reason is that people with diabetes do not realize that they have diabetes, so there is a need for early detection in knowing this. The purpose of this research is to improve the performance of the Hard Voting Classifier model combining the Decision Tree, Random Forest, and XGBoost algorithms with the ADASYN oversampling technique that handles data imbalance in diabetes prediction. This study uses patient information data with a total of 1000 data and 14 features from the Medical City Hospital laboratory, Iraq. The results of this study show an increase in the performance of the prediction model with an accuracy value of 99.0%, precision 99.1%, recall 99.0%, and f1-score 98.98% without using ADASYN. Then get an accuracy value of 99.8%, precision 99.8%, recall 99.8%, and f1-score 99.8% by using ADASYN as an oversampling technique. This shows that there is an increase in the performance of the Hard Voting Classifier model so that it produces accurate predictions of diabetes, where the correctness of diabetes prediction is very good.
Optimasi Convolutional Neural Networks untuk Deteksi Kanker Payudara menggunakan Arsitektur DenseNet Mas'ud, Ryan Ali; Junta Zeniarja
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 1 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i1.25883

Abstract

Breast cancer is a disease commonly suffered by women worldwide, ranking as the second-largest disease burden. In response to the urgent need for improved detection accuracy, Convolutional Neural Networks (CNNs) promise significant advancements. The objective of this research is to optimize the use of CNNs with the DenseNet architecture for breast cancer detection. The study employs quantitative methods, leveraging Deep Learning through CNNs. Mammography data is sourced from Kaggle, specifically the “Breast Histopathology Images” dataset. This dataset comprises 90,000 digital mammography images, which are preprocessed and divided proportionally for training, validation, and model testing. Research variables encompass CNN model parameters, training techniques, and the integration of imaging modalities to enhance breast cancer detection performance. The research focuses on processed mammography data, with accuracy and image quality as key evaluation metrics for breast cancer sample identification. Our findings demonstrate that the DenseNet architecture within CNNs achieves an impressive 92% accuracy in breast cancer detection. This remarkable performance signifies success in enhancing image quality and class prediction, aligning with the DenseNet architecture’s flow diagram. Ultimately, these results contribute significantly to effective breast cancer diagnosis by optimizing CNNs with the DenseNet architecture to improve image quality during breast cancer sampling.
Perancangan Aplikasi Kamus Istilah Jawa Berbasis Android sebagai Upaya Pelestarian Budaya Jawa Muljono, Muljono; Rokhman, Nur; Zeniarja, Junta; Nugroho, Raden Arief; Suryaningtyas, Valentina Widya; Aryanto, Bayu
ANDHARUPA: Jurnal Desain Komunikasi Visual & Multimedia Vol. 9 No. 04 (2023): December 2023
Publisher : Dian Nuswantoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/andharupa.v9i4.9282

Abstract

AbstrakSalah satu bahasa daerah di Indonesia yang paling beragam dan kaya kosakatanya adalah bahasa Jawa.  Namun, seringkali sulit bagi orang memahami arti istilah-istilah Jawa. Tujuan penelitian ini adalah untuk mengembangkan aplikasi kamus digital istilah Jawa yang akan membantu pengguna memahami dan menggunakan istilah Jawa.  Aplikasi dikembangkan dalam penelitian ini memungkinkan akses cepat dan mudah bagi pengguna dalam mencari istilah Jawa beserta definisi, contoh penggunaan, dan informasi terkait lainnya. Aplikasi ini dilengkapi fitur-fitur tambahan seperti pengucapan audio dan fitur urun daya yang memungkinkan masyarakat dapat menambah database tetapi tetap menunggu validasi dari pengelola aplikasi.  Dalam pengembangan aplikasi kamus digital ini menggunakan metode waterfall dan metode blackbox untuk metode pengujiannya.  Penelitian ini menghasilkan aplikasi kamus digital bahasa Jawa yang bernama "Senarai Istilah Jawa" yang bertujuan untuk membantu masyarakat memahami dan menggunakan istilah Jawa dan sebagai salah satu bentuk upaya membantu pelestarian bahasa daerah di Indonesia. Kata Kunci: aplikasi android, budaya, kamus istilah Jawa, metode waterfall AbstractOne of the regional languages in Indonesia that is most diverse and rich in vocabulary is Javanese. However, it is often difficult for people to understand the meaning of Javanese terms. The aim of this research is to develop a digital dictionary application of Javanese terms that will help users understand and use Javanese terms. The application developed in this research allows users quick and easy access to search for Javanese terms along with definitions, usage examples and other related information. This application is equipped with additional features such as audio pronunciations and a crowdsourcing feature that allows people to add to the database but still wait for validation from the application manager. In developing this digital dictionary application, the waterfall method and black box method were used for testing methods. This research produces a digital Javanese dictionary application called "Senarai Istilah Jawa" which aims to help people understand and use Javanese terms and as a form of effort to help preserve regional languages in Indonesia. Keywords: android application, culture, dictionary of Javanese terms, waterfall method 
Optimization Chatbot Services Based on DNN-Bert for Mental Health of University Students Dzaky, Azmi Abiyyu; Zeniarja, Junta; Supriyanto, Catur; Shidik, Guruh Fajar; Paramita, Cinantya; Subhiyakto, Egia Rosi; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7403

Abstract

Attention to mental health is increasing in Indonesia, especially with the recent increase in the number of cases of stress and suicide among students. Therefore, this research aims to provide a solution to overcome mental health problems by introducing a chatbot system based on Deep Neural Networks (DNN) and BiDirectional Encoder Representation Transformers (BERT). The primary objective is to enhance accessibility and offer a more effective solution concerning the mental health of students. This chatbot utilizes Natural Language Processing (NLP) and Deep Learning to provide appropriate responses to mild mental health issues. The dataset, comprising objectives, tags, patterns, and responses, underwent processing using Indonesian language rules within NLP. Subsequently, the system was trained and tested using the DNN model for classification, integrated with the TokenSimilarity model to identify word similarities. Experimental results indicate that the DNN model yielded the best outcomes, with a training accuracy of 98.97%, validation accuracy of 71.74%, and testing accuracy of 71.73%. Integration with the TokenSimilarity model enhanced the responses provided by the chatbot. TokenSimilarity searches for input similarities from users within the knowledge generated from the training data. If the similarity is high, the input is then processed by the DNN model to provide the chatbot response. This integration of both models has proven to enhance the responsiveness of the chatbot in providing various responses even when the user inputs remain the same. The chatbot also demonstrates the capability to recognize various inputs more effectively with similar intentions or purposes. Additionally, the chatbot exhibits the ability to comprehend user inputs although there are many writing errors.
Co-Authors Abu Salam Abu Salam Adhitya Nugraha Adhitya Nugraha Adi Wibowo Afridiansyah, Rahmanda Agus Winarno Agus Winarno, Agus Ahmad Alaik Maulani Ailsa Nurina Cahyani Alya Nurfaiza Azzahra Anisatawalanita Ukhifahdhina Anugrah, Muhammad Ikhsan Ardytha Luthfiarta Ardytha Luthfiarta Asih Rohmani Asih Rohmani Asih Rohmani Atika Rahmawati Bayu Aryanto Budi Warsito Cahyani, Ailsa Nurina Candra, Rejka Aditya Catur Supriyanto Catur Supriyanto Debrina Luna Arghata Mangkawa Deby Arida NiMatus Sa’adah Devi Ayu Rachmawati Dianti, Reza Nur Diyan Adiatma Dzaky, Azmi Abiyyu Edi Faisal Edi Sugiarto Edi Sugiarto Edi Sugiarto Egia Rosi Subhiyakto, Egia Rosi Erwin Yudi Hidayat Esmi Nur Fitri Esmi Nur Fitri Esmi Nur Fitri Fajarudin Zakariya Farda Alan Ma'ruf Farda Alan Ma’ruf Ferry Bintang Nugroho Fikri Budiman Fikri Budiman Firmansyah, Gustian Angga Ganiswari, Syuhra Putri Guruh Fajar Shidik Haresta, Alif Agsakli Harun Al Azies Ida Ayu Putu Sri Widnyani Ika Novita Dewi Jaya, Sava Irhab Atma Khoirunnisa, Emila Kiki Widia Kurniawan Ridwan Surohardjo Kurniawan, Defri L. Budi Handoko Luh Putu Ratna Sundari Lutfi Kharisma M Hafidz Ariansyah M. Hafidz Ariansyah Manurung, Ayub Michaelangelo Mas'ud, Ryan Ali Maulani, Ahmad Alaik Mufida Rahayu Muhammad Jamhari Muhammad Joyo Satrio Muljono Muljono Muljono, - Nabila, Qotrunnada Nitho Alif Ibadurrahman Novi Hendriyanto Nur Rokhman Nur Rokhman Octaviani, Dhita Aulia Paramita, Cinantya Pratama, Rifky Ariya Pulung Nurtantio Andono Putra, Vander Mulya Putri, Rusyda Tsaniya Eka Raden Arief Nugroho Rama Eka Saputra Ramadhan Rakhmat Sani Ramadhan, Ahnaf Irfan Ramadhan, Muhammad Eky Restu Agung Pamuji Rezaroebojo, Rizal Riyan Ardiansyah Rohman, Adib Annur Savicevic, Anamarija Jurcev Setiawan, Dicky Setiawan Sindhu Rakasiwi Sri Winarno Sri Winarno Sri Winarno Syabilla, Mutiara Utomo, Danang Wahyu Valentina Widya Suryaningtyas, Valentina Widya Wibowo Wicaksono Wibowo Wicaksono