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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) IT JOURNAL RESEARCH AND DEVELOPMENT Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Ipteks Terapan Journal of Applied Engineering and Technological Science (JAETS) Jurnal Nasional Teknik Elektro dan Teknologi Informasi JAIA - Journal of Artificial Intelligence and Applications SATIN - Sains dan Teknologi Informasi Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika

Erlin

Institut Bisnis Dan Teknologi Pelita Indonesia

Author-ID : 2644270

Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics

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Deteksi Dini Penyakit Diabetes Menggunakan Machine Learning dengan Algoritma Logistic Regression Erlin; Yulvia Nora Marlim; Junadhi; Laili Suryati; Nova Agustina
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 2: Mei 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Diabetes is one of the deadliest diseases in the world, including in Indonesia. It can cause complications in numerous body parts and increase the overall risk of death. One way to detect diabetes is to use machine learning algorithms. Logistic regression is a classification model in machine learning widely used in clinical analysis. In this paper, a predictive model was created in Python IDE using logistic regression to conduct an early detection if a person has diabetes or not depending on the initial data provided. The experiment was carried out using a dataset from the Pima Indians Diabetes Database, which consisted of 768 patient data with eight independent variables and one dependent variable. Exploratory data analysis was applied to obtain maximum insight of the datasets owned by using statistical assistance and presenting them through visual techniques. Some dataset variables contained incomplete data. Missing data values were replaced with the median value of each variable. Unbalanced data was handled using the synthetic minority over-sampling technique (SMOTE) to increase the minority class through synthetic data sampling. The model was evaluated based on the confusion matrix, which showed a reasonably good performance with an accuracy value of 77%, precision of 75%, recall of 77%, and F1-score of 76%. In addition, this paper also used the grid search technique as a hyperparameter tuning that could improve the performance of the logistic regression model. The primary model performance with the model after applying the grid search technique was tested and evaluated. The experimental results showed that the hyperparameter tuning-based model could improve the performance of the logistic regression algorithm for prediction with an accuracy value of 82%, precision of 81%, recall of 79%, and F1-score of 80%.

Neural Network Method in Text Message Categorization of Online Discussion Erlin; Johan; Triyani Arita Fitri; Agustin; Hamdani
JAIA - Journal of Artificial Intelligence and Applications Vol. 1 No. 2 (2021): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

This paper presents research in neural network approach for text messages categorization of collaborative learning skill in an online discussion. Although a neural network is a popular method for text categorization in the research area of machine learning, unfortunately, the use of neural network in educational settings is rare. Usually, text categorization by neural network is employed to categorize news articles, emails, product reviews, and web pages. In an online discussion, text categorization that is used to classify the message sent by the student into a certain category is often manual, requiring skilled human specialists. However, human categorization is not an effective way for a number of reasons; time- consuming, labor-intensive, lack of consistency in a category, and costly. Therefore, this paper proposes a neural network approach to code the message automatically. Results show that neural networks achieving useful classification on eight categories of collaborative learning skills in an online discussion as measured based on precision, recall, and balanced F-measure.

Sentiment Analysis of Citayam Fashion Week Phenomenon Using Support Vector Machine Muhammad Rosyadi; Erlin
IT Journal Research and Development Vol. 7 No. 2 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.12188

Citayam Fashion Week is a phenomenon that displays a model doing a fashion show using distinctive and unique clothing when crossing a zebra cross as a catwalk. This phenomenon has received extraordinary attention and discussion from various circles and led to numerous pros and cons among the public and observers of society in Indonesia. Therefore, it is great importance to conduct the study on sentiment analysis of this phenomenon to determine society's sentiment tendency to provide government references and help decision-makers improve their policies. Sentiment analysis was performed using the Support Vector Machine based on the polynomial kernel. The results shows that the accuracy, recall, precision and F1-Score value of 95.61%, 95.66%, 96% and 95.55%, respectively. This study proved that the Support Vector Machine classifier with the polynomial kernel provides higher algorithm performance on text classification. Therefore, the government can use the result of this study to evaluate the existence of the citayam fashion week which may be followed by other phenomena.

Analisis Sentimen Prosesor AMD Ryzen menggunakan Metode Support Vector Machine Erlin; Josef Sianturi; Alyauma Hajjah; Agustin
SATIN - Sains dan Teknologi Informasi Vol 7 No 2 (2021): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Prosesor AMD menjadi salah satu pesaing prosesor Intel semenjak dikeluarkannya prosesor Ryzen generasi 3. Berbagai pendapat dan opini masyarakat mengenai prosesor ini sangat mudah ditemui pada media sosial Twitter. Opini ini dapat digunakan sebagai sistem untuk mendukung keputusan berkaitan dengan produk AMD Ryzen. Tujuan penelitian ini adalah untuk mengimplementasikan Analisis Sentimen dalam pendekatan Data Mining untuk menganalisa tekstual data yang terdapat pada Twitter menggunakan metode Support Vector Machine, mengeksplorasi dan memahami tren opini publik mengenai prosesor AMD Ryzen dan mengklasifikasikannya kedalam polaritas biner. Penelitian ini menggunakan Tweet dari Library Tweetscrapper. Pelabelan dilakukan oleh expert untuk diklasifikasikan menjadi sentimen positif dan negatif. Selanjutnya melakukan pra-pemrosesan data untuk menghilangkan noise, mendeteksi nilai data yang hilang, data duplikat dan tidak relevan. Selanjutnya, algoritma machine learning digunakan untuk memprediksi data baru. Model yang dihasilkan dievaluasi menggunakan confusion matrix. Hasil penelitian menunjukkan bahwa kinerja metode SVM sangat baik dalam hal akurasi, presisi, recall dan F1 Score dengan nilai masing-masing 96,67%, 96,43%, 100% dan 98,18%. Berdasarkan hasil yang diperoleh, sebagian besar publik memiliki opini yang positif terhadap prosesor AMD Ryzen. Penelitian ini juga membuktikan bahwa metode Support Vector Machine dapat digunakan sebagai algoritma cerdas untuk memprediksi sentimen di Twitter untuk data baru dengan cepat dan akurat.

A Machine Learning Model for Determination of Gender Utilizing Hybrid Classifiers Dewi Nasien; M. Hasmil Adiya; Yusnita Rahayu; Dahliyusmanto Dahliyusmanto; Erlin Erlin; Devi Willieam Anggara
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v5i1.1839

One part of forensic anthropology involves investigating skeletal remains to identify corpses, and many of these remains were found incomplete, burned, broken, or destroyed, making investigation challenging. This study aims to use the pelvis and femur to identify the gender of skeletal remains. The pelvis and femur have previously been proven to be accurate indicators of a corpse's gender. The identification process is done through the measurement of the subpubic angle of the pelvis and the angle taken straight down from the top of the femur to the patella and then straight up. The two measurements were combined using the principal component analysis (PCA) method into two attributes on the x and y axes. These attributes were later used as data for the machine learning model design. The design process consisted of an Artificial Neutral Network (ANN) design model and Support Vector Machine (SVM) design model combined into a hybrid machine learning system. The ANN and SVM hybrid machine learning were tested with acquired data. The result of the test using the confusion matrix showed 83.33% accuracy, which is categorized as "good classification" based on Area Under the Curve (AUC).

Analysis and Identification of Non-Impact Factors on Smart City Readiness Using Technology Acceptance Analysis: A Case Study in Kampar District, Indonesia M. Khairul Anam; Arda Yunianta; Hasan J. Alyamani; Erlin Erlin; Ahmad Zamsuri; Muhammad Bambang Firdaus
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v5i1.2401

Most countries start to implement Smart Cities as an innovation for urban strategy. However, not all Smart Cities implementations worked and were implemented well, because the community still not ready for the implementation of Smart City. The aim of this research is to investigate community readiness and finding low impact factors for implementing smart cities based on 5 factors, namely AU, PEOU, ATU, BIU, and PU. This research was using a qualitative study with the Technology Acceptance Model approach (TAM) to investigate the relationship between 5 factors. Based on the results of data distribution, there are 2 clusters, namely people who know about public service applications and people who are not aware of any public service applications. Furthermore, there are 3 tests conducted in this research namely T-test, F-test and Coefficient Determination Test to determine the impact and influence of the relationship between each factor. However, from the results of the t-test it was found that there were 2 relationships that had no impact because the t-count was negative and the 2 relationships between these factors were between PU - AU and AU - PU.

Soursop Leaf Disease Detection With CNNs: From Training to Deployment Hidayatullah Nuriadi, Siti; Sabri, Erlin; Hajjah, Alyauma; Noratama Putri, Ramalia
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/sn8avr92

Soursop (Annona muricata) is a valuable tropical fruit crop that is highly susceptible to leaf diseases caused by fungal, bacterial, and viral infections. These diseases can significantly impact crop yield and quality, posing challenges for farmers, especially when early detection is delayed. This study proposes an automated solution using Convolutional Neural Networks (CNNs) to detect soursop leaf diseases through image classification. A dataset of 400 labelled leaf images, including healthy and diseased leaves (Leaf Rust, Leaf Spot, and Sooty Mold), was collected and preprocessed for the dataset. Three CNN architectures—MobileNetV2, VGG19, and ResNet50—were evaluated based on accuracy, precision, recall, and F1-score. Among them, MobileNetV2 outperformed the others, achieving 73% accuracy, 72% precision, 65% recall, and 66% F1-score and demonstrated strong consistency across classes. The best-performing model was deployed using the Flask web framework, enabling users to upload soursop leaf images and receive instant disease classification along with suggested treatments and preventive measures. This study’s novelty lies in the end-to-end pipeline, from model training to deployment via Flask, providing a ready-to-use solution for farmers.

Penerapan Linear Discriminant Analysis Untuk Meningkatkan Kinerja Algoritma Support Vector Machine Gusrianty, Gusrianty; Fenly, Fenly; Jollyta, Deny; Erlin, Erlin; Putri, Ramalia Noratama; Oktariana, Dwi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8772

Obesity is a complex chronic disease influenced by various factors, such as genetic, environmental, and lifestyle, which is characterized by excess body weight due to the excessive accumulation of body fat. With the rapid advancement of technology and digitalization across all sectors, data has become increasingly vital, as large datasets generate valuable information. However, a key challenge in data analysis is addressing redundancy, noise, and high dimensionality, which can affect the performance of machine learning algorithms. This study aims to investigate the effectiveness of combining Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) in enhancing the accuracy and efficiency of high-dimensional data classification, particularly in predicting obesity levels. LDA is employed to reduce data dimensionality while retaining the most relevant features, whereas SVM is utilized as the classification algorithm to predict obesity levels based on patterns identified within the dataset. The research was conducted using a dataset consisting of 779 training samples and 195 testing samples. The results reveal that the combination of LDA and SVM achieved a classification accuracy of up to 99%, with a 50% reduction in data dimensionality and a computation speed of 0,0696 second. Moreover, computation time was significantly reduced, indicating that LDA not only facilitates data simplification but also improves the overall efficiency of the classification process.

Analisis Sentimen Terhadap Ulasan Aplikasi IKD di Play Store Menggunakan Random Forest Kelvin H.; Erlin; Yenny Desnelita; Dwi Oktarina
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 3: Agustus 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i3.20473

The rapid growth of digital applications in population administration services has increased the importance of sentiment analysis to understand user perceptions more deeply. This study focuses on the Digital population identity (Identitas Kependudukan Digital, IKD), a digital identity application developed by the Indonesian government. It aims to classify user reviews of the IKD application into positive, neutral, and negative sentiments using the random forest algorithm. The dataset consisted of 28,134 user reviews from the Google Play Store, including usernames, review texts, timestamps, and star ratings. The research stages included data preprocessing, labeling, handling missing values, and text processing (cleansing, tokenizing, stopword removal, and stemming). The data were divided into 80% training and 20% testing sets. The best-performing model used the parameters: max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, and n_estimators=300, achieving an average accuracy of 83.78%. To address class imbalance, the synthetic minority oversampling technique (SMOTE) was applied, resulting in improved performance with an accuracy of 86.29%. Evaluation metrics before SMOTE showed 83.85% accuracy, 80.40% precision, 83.85% recall, and 81.73% F1 score. After SMOTE, precision increased to 81.22%, while accuracy and recall slightly decreased to 80.86%, with an F1 score of 81.03%. Furthermore, sentiment trend analysis using N-gram techniques (unigram, bigram, trigram) was conducted to identify frequently mentioned topics and user concerns. These insights support the research objective of guiding application improvements aligned with user needs and enhancing the overall digital service experience.

Dampak SMOTE terhadap Kinerja Random Forest Classifier berdasarkan Data Tidak seimbang Erlin Erlin; Yenny Desnelita; Nurliana Nasution; Laili Suryati; Fransiskus Zoromi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1726

Dalam aplikasi machine learning sangat umum ditemukan kumpulan data dalam berbagai tingkat ketidakseimbangan mulai dari ketidakseimbangan kecil, sedang sampai ekstrim. Sebagian besar model machine learning yang dilatih pada data tidak seimbang akan memiliki bias dengan memberikan tingkat akurasi yang tinggi pada kelas mayoritas dan sebaliknya rendah pada kelas minoritas. Tujuan penelitian ini adalah untuk mengevaluasi dampak dari SMOTE (Synthetic Minority Oversampling Technique) pada pengklasifikasi Random Forest untuk memprediksi penyakit jantung. Data berjumlah 299 berasal dari UCI Machine learning Repository digunakan untuk membangun model prediksi berdasarkan 12 variabel independen dan 1 variabel dependen. Kelas minoritas dalam dataset pelatihan di oversampling menggunakan teknik SMOTE (Synthetic Minority Oversampling Technique). Model dievaluasi tidak hanya menggunakan ukuran kinerja Accuracy dan Precision saja, namun juga menggunakan alternatif ukuran kinerja lainnya seperti Sensitivity, F1-score, Specificity, G-Mean dan Youdens Index yang lebih baik digunakan untuk data yang tidak seimbang. Hasil penelitian menunjukkan bahwa teknik SMOTE (Synthetic Minority Oversampling Technique) mampu mengurangi overfitting sekaligus meningkatkan kinerja model Random Forest pada semua indikator. Peningkatan skor Accuracy sebesar 3.45%, Precision 4.8%, Sensitivity 7.1%, F1-score 4.8%, Specificity 2.1%, G-Mean 4.4%, dan Youdens Index 6.3%. Penelitian ini membuktikan bahwa dalam menentukan pengklasifikasi dengan algoritma machine learning seperti Random Forest, kemiringan kelas dalam data perlu diperhitungkan dan diseimbangkan untuk hasil kinerja yang lebih baik.

Co-Authors Adiya, M. Hasmil Agustin Ahmad Zamsuri Alyauma Hajjah Anam, M Khairul Arda Yunianta Boby Hasbul Fikri Dahliyusmanto, Dahliyusmanto Deny Jollyta Desnelita, Yenny Devi Willieam Anggara Dewi Nasien Dian Puspita Didik Sudyana Dwi Oktarina Fenly, Fenly Fransiskus Zoromi Gusrianty, Gusrianty Hamdani - Hanafiah, Anggi Hasan J. Alyamani Herwin Herwin, Herwin Hidayatullah Nuriadi, Siti Johan Josef Sianturi Junadhi Junadhi Kelvin H. Khusaeri Andesa Laili Suryati M. Wino Adi Putra Marlim, Yulvia Nora Muhammad Bambang Firdaus Muhammad Fatroni, Muhammad Muhammad Rosyadi Nora Lizarti Noratama Putri, Ramalia Nova Agustina Nurliana Nasution, Nurliana Oktariana, Dwi Rahmaddeni Rahmaddeni Rahmiati Ramalia Noratama Putri Reny Marissa Rohana Yola Parastika Hutasoit Sugiono Sugiono Susanti, Susanti Triyani Arita Fitri Unang Rio Yusnita Rahayu Yusuf Nuron Zukri Julisman

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