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All Journal International Journal of Evaluation and Research in Education (IJERE) Journal of Information Systems Engineering and Business Intelligence Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Systems and Informatics Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Afrig Aminuddin
Universitas AMIKOM Yogyakarta
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Implementasi Unified Modeling Language (UML) pada Perancangan Aplikasi WiFiTalkie Berbasis TCP/IP Afrig Aminuddin
Sistemasi: Jurnal Sistem Informasi Vol 8, No 2 (2019): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (745.027 KB) | DOI: 10.32520/stmsi.v8i2.484

Abstract

Di dunia komunikasi analog kita mengenal perangkat yang bernama HT (Handy Talkie). Perangkat ini bekerja dengan menggunakan sinyal elektromagnetik pada frekuensi radio tertentu. Perangkat ini berfungsi sebagai pengirim dan penerima sinyal radio.Sinyal yang dikirimkan adalah sinyal suara yang telah diubah menjadi sinyal elektromagnetik. Untuk dapat berkomunikasi satu sama lain, maka harus ada kesepakatan antar pengguna untuk menyetel perangkatnya pada frekuensi yang sama. Salah satu kelemahan dari perangkat ini adalah kualitas suara yang cenderung noisy dan sangat bergantung pada kondisi cuaca. Dengan seiring teknologi semikonduktor yang berkembang pesat, terciptalah perangkat digital yang semakin bervariasi kegunaannya. Saat ini sudah banyak diciptakan perangkat digital yang dapat menggantikan perangkat analog secara keseluruhan. Sebagai contohnya adalah pesawat televisi. Pesawat televisi digital memberikan kualitas yang jauh lebih baik daripada perangkat televisi analog dengan ukuran yang jauh lebih ramping. Contoh yang lain saat ini tersedia smartphone yang memiliki fitur yang sangat lengkapyang tertanam pada perangkat yang berukuran relatif kecil. Salah satu fiturnya adalah wifi. Dengan fitur ini sebuah smartphone dapat terhubung satu sama lain, bahkan dapat terhubung dengan internet dengan mudahnya. Dalam rangka digitalisasi perangkat analog dan tersedianya fiturwifidi dalam smartphone ini, maka diciptakan sebuah aplikasi WiFiTalkie. Cara kerjanya yaitu smartphone akan mengirimkan sinyal suara dengan memanfaatkan teknologi TCP/IP melalui jaringan wifi, kemudian smartphone yang lain di dalam network yang sama menerima sinyal ini dan memprosesnya kembali menjadi sinyal suara. Aplikasi ini dibangun dengan mengimplementasikan metode perancangan Unified Modeling Language (UML). Hasil penelitian ini menunjukkan bahwa kualitas suara yang dihasilkan oleh WiFiTalkie jauh lebih baik daripada HT yang berbasis pada sinyal analog.
Rancang Bangun Aplikasi Smart Touring Berbasis Android Majid Rahardi; Afrig Aminuddin
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 21 No 1 (2021)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (517.16 KB) | DOI: 10.30812/matrik.v21i1.1185

Abstract

Perkembangan teknologi saat ini begitu cepat. Teknologi membuat perubahan pada peradaban manusia. Telah banyak kegiatan manusia yang didukung oleh kemajuan teknologi. Tak terkecuali kegiatan touring yang dilakukan bersama-sama. Touring adalah kegiatan berkendara dari suatu tempat ke tempat lain secara bersama-sama. Saat ini komunitas touring terus meningkat, namun masih memiliki beberapa permasalahan saat melakukan aktifitasnya. Saat ini salah satu permasalahan yang ada pada aktifitas touring adalah pengendara satu dengan yang lainnya tidak bisa mengetahui lokasi semua teman touring mereka. Oleh karena itu sangat dimungkinkan ada anggota touring mereka yang tertinggal jauh atau salah jalur. Dengan teknologi smartphone yang sangat pesat, dimungkinkan dibangun sebuah sistem yang dapat mendukung kegiatan touring tersebut. Pada penelitian ini telah berhasil dibangun sistem yang dapat mendukung kelancaran aktifitas touring. Fokus penelitian ini adalah membangun sistem touring yang dapat mendeteksi keberadaan semua member touring ketika sedang melakukan kegiatan touring. Sistem yang dibangun adalah berbasis contextual awareness, yaitu sistem yang mampu memberikan informasi kepada pengguna dengan data yang didapat dari lingkungannya. Dalam hal ini adalah memberikan informasi ketika ada member touring yang berjauhan dengan member touring lainnya.
Sentiment Analysis and Topic Modeling of Kitabisa Applications using Support Vector Machine (SVM) and Smote-Tomek Links Methods I Nyoman Switrayana; Diki Ashadi; Hairani Hairani; Afrig Aminuddin
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 2 No 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3406

Abstract

Kitabisa is an Indonesian application that functions to raise funds online. Users can easily support various types of campaigns and donate funds to various social causes through the app. User reviews of the application are very diverse, and it is not sure whether user reviews of the application tend to be positive, neutral, or negative. This research aimed to analyze the sentiment of the Kitabisa application by modeling topics using Latent Dirichlet Allocation (LDA) and classifying user reviews using a Support Vector Machine (SVM). The scrapped dataset showed imbalanced dataset problems, so the SMOTE-Tomek Links oversampling technique was proposed. The results of this study show that using LDA produces five topics often discussed in 750 reviews. Then, the performance of SVM without using SMOTE-Tomek Links was 72% accuracy, 76% precision, 72% recall, and 64% f1 score. Meanwhile, using SMOTE-Tomek Links could significantly improve the performance, namely 98% accuracy, 98% precision, 98% recall, and 98% f1 score. Based on this research, the application of SVM achieved high performance for user sentiment classification, especially when the dataset was in a balanced state. Therefore, the SMOTE-Tomek Links oversampling technique is recommended for dealing with unbalanced sentiment datasets.
Comparison of Parametric and Nonparametric Forecasting Methods for Daily COVID-19 Cases in Malaysia Agastya, I Made Artha; Aminuddin, Afrig
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1819

Abstract

Numerous research studies are currently examining various measures to control the transmission of COVID-19. One essential task in this regard is predicting or forecasting the number of infected individuals. This predictive capability is crucial for governments to allocate resources effectively. However, the most effective approach to handling time series problems between the parametric and non-parametric methods is unclear. The parametric method utilizes a fixed number of parameters to calculate the value. On the other hand, the non-parametric method increases its parameters along with the number of observations. To address the issue, we conducted a study comparing parametric and non-parametric models for time series forecasting, specifically using Malaysia's daily confirmed COVID-19 cases from 18/3/2020 to 30/12/2020. Since there have been limited comparisons of these models in time series forecasting, we believe our study is beneficial. We considered various models, including persistence, autoregression, ARIMA, SARIMA, single, double, and triple exponential smoothing, multi-linear regression, support vector regression, artificial neural networks (ANN), K-nearest neighbor regression, decision trees regression, random forest regression, and Gaussian processes regression models. Our study revealed significant characteristics of these methods, and we found that exponential smoothing methods were the most effective in capturing the level and trend of the data compared to other methods. Additionally, ANN had the least forecasting error among the machine learning methods. In conclusion, non-parametric methods are not suitable for predicting daily cases of Covid-19 in Malaysia. Enhancing the parametric methods will be preferable in the future.  
Leveraging Various Feature Selection Methods for Churn Prediction Using Various Machine Learning Algorithms Kusnawi, Kusnawi; Ipmawati, Joang; Asadulloh, Bima Pramudya; Aminuddin, Afrig; Abdulloh, Ferian Fauzi; Rahardi, Majid
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2453

Abstract

This study aims to examine the effect of customer experience on customer retention at DQLab Telco, using machine learning techniques to predict customer churn. The study uses a dataset of 6590 customers of DQLab Telco, which contains various features related to their service usage and satisfaction. The data includes various features such as gender, tenure, phone service, internet service, monthly charges, and total charges. These features represent the demographic and service usage information of the customers. The study applies several feature selection methods, such as ANOVA, Recursive Feature Elimination, Feature Importance, and Pearson Correlation, to select the most relevant features for churn prediction. The study also compares three machine learning algorithms, namely Logistic Regression, Random Forest, and Gradient Boosting, to build and evaluate the prediction models. This study finds that Logistic Regression without feature selection achieves the highest accuracy of 79.47%, while Random Forest with Feature Importance and Gradient Boosting with Recursive Feature Elimination achieve accuracy of 77.60% and 79.86%, respectively. The study also identifies the features influencing customer churn most, such as monthly charges, tenure, partner, senior citizen, internet service, paperless billing, and TV streaming. The study provides valuable insights for DQLab Telco in developing customer churn reduction strategies based on predictive models and influential features. The study also suggests that feature selection and machine learning algorithms play a vital role in improving the accuracy of churn prediction and should be customized according to the data context.
Optimizing students’ practical skills through project-based learning: case study in vocational high schools Dzulkurnain, Mohammad Iskandar; Aminuddin, Afrig; Hammood, Waleed A.; Abdullah, Khairul Hafezad; Alam Miah, Mohammad Badrul
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i5.28694

Abstract

Competition in the global era requires graduates from vocational schools to be more skilled in hard and soft skills to adapt to the industrial world. Adaptation of vocational education institutions to the industrial world is vital; thus, they can continuously update the skills of their graduate candidates. Hence, this research aims to describe the implementation of the Center of Excellence curriculum and project-based learning in a vocational high school as a form of school adaptation to the development of the industrial world in the 21st century. This research was included in a qualitative research design adopting a case study. The research respondents consisted of vocational high school residents in Central Java. Data was collected through interview techniques and observations and then analyzed interactively and descriptively. The research results then reported that the school was fully committed to implementing the Center of Excellence curriculum regarding teaching human resources and learning facilities. The project-based learning process also seemed to run optimally. Students could accept it, considering that project-based learning was implemented because it was an adaptive model to accommodate 21st century competencies. However, there is still room for improvement and optimization in order to effectively implement this operational curriculum and enhance students' ability to acquire 21st century skills.
Unlocking the Potential of OLT for Startup ISPs in Indonesia: Challenges and Strategies Mustofa, Dinar; Saputra, Dhanar Intan Surya; Kusuma, Velizha S; Aminuddin, Afrig; Wirasto, Anggit; Apitiadi, Satyo Dwi
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.943

Abstract

This study explores the implementation of Optical Line Terminal (OLT) technology by Internet Service Providers (ISPs) startups in underserved and remote areas of Indonesia, examining its effectiveness, challenges, and opportunities. The research reveals that OLT technology can significantly improve internet service quality, with measurable increases in speed (up to 30%) and reliability (20% improvement), especially in rural areas. However, ISP startups face several technical challenges, including inadequate fiber optic infrastructure, high initial investment costs, and the complex geographical conditions across Indonesia’s diverse islands. Regulatory barriers, such as lengthy licensing processes and inconsistent policies, further hinder the deployment of OLT technology. Despite these challenges, the study identifies key opportunities for ISP startups to overcome these obstacles. Collaboration with government initiatives like the Palapa Ring and the potential integration with 5G and IoT technologies can reduce costs and accelerate network deployment. Additionally, leveraging existing infrastructure enables faster expansion of broadband services, particularly in remote regions. The research also finds that ISP startups adopting OLT technology can significantly narrow the digital divide by expanding service coverage in underserved areas, with a noted 25% increase in digital inclusion. These findings offer valuable insights for policymakers and business leaders, informing strategies to optimize OLT technology and foster a more equitable digital transformation across Indonesia, particularly in expanding access to broadband internet in marginalized regions.
Performance Analysis Of Machine Learning Algorithms Using The Ensemble Method On Predicting The Impact Of Inflation On Indonesia's Economic Growth Abdulloh, Ferian Fauzi; Aminuddin, Afrig; Rahardi, Majid; Harianto, Fetrus Jari
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2567

Abstract

The warning of a global recession expected in 2023 is currently the world's concern. Global financial institutions have raised interest rates to lower inflation, which has led to this problem. This study aims to evaluate the effect of interest rates and inflation on Indonesia's economic growth and compare the performance of machine learning models, specifically Random Forest and XGBoost, in analyzing the impact of inflation. A qualitative methodology was used for the literature survey, while the quantitative approach involved the implementation of machine learning algorithms using the Ensemble Method. The results show that Random Forest performs better than XGBoost in predicting the impact of inflation on economic growth, with MSE values of 0.799 and 0.864 and MAE of 0.576 and 0.619, respectively. In addition, the R-squared value of Random Forest 0.908 is also higher than that of XGBoost 0.901, indicating that the model can better explain the variation in the target data. The practical implication of this study is that the Random Forest model can be more effectively used in analyzing the impact of inflation on Indonesia's economic growth. Recommendations for future research include exploring other methods and using more extended time series to deepen the understanding of the relationship between interest rates, inflation, and economic growth.
Enhanced Predictive Modeling for Non-Invasive Liver Disease Diagnosis Prabowo, Donni; Bety Wulan Sari; Yoga Pristyanto; Afrig Aminuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6449

Abstract

Liver diseases (e.g. cirrhosis, hepatitis, and fatty liver disease) are globally one of the leading causes of mortality and are typically diagnosed in advanced stages due to vague symptoms and the difficulty involved in existing diagnostic techniques (e.g. biopsies). To optimize the early diagnosis of liver disease, this study proposes an enhanced, non-invasive approach using machine learning techniques. The research is enriched with a full pipeline, from exploratory data analysis and imputation of the dataset, treatment of the outlier, encoding of labels and scaling using ILPD (Indian Liver Patient Dataset). The classification models compared were RandomForest, XGBoost, LGBM, and CatBoost. The CatBoost algorithm fine-tuned with RandomizedSearchCV showed the highest performance with a test accuracy of 93%. The performance was again better than any already published methods showing that advanced ensembling and hyperparameter optimization worked. The proposed model is suitable for incorporation into clinical decision support systems and provides reliable and accurate diagnostic assistance. In addition to its high accuracy, the model is robust for missing and categorical data, which is a challenge in any real-world clinical scenario. These findings add to the growing body of evidence supporting AI-based medical diagnostics and suggest that CatBoost is a highly promising tool for facilitating timely screening and diagnosis of liver disease. Furthermore, the study stresses the need for thorough preprocessing and cross-validation, which serve to reduce biases that are present in widely applied datasets. Ongoing future efforts may involve the integration of multi-source data and implementation of explainable AI techniques to allow for wider clinical trust and use.
Optimization and Collaboration of Fuzzy C-Mean, K-Mean, and Naïve Bayes Algorithms Using the Elbow Method for Micro, Small, and Medium Enterprises Norhikmah, -; Nurastuti, Wiji; Aminuddin, Afrig; Sidauruk, Acihmah; Gunawan, Puguh Hasta
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3292

Abstract

Micro, Small, and Medium Enterprises (SMEs) have a vital role in Indonesia’s economy. However, IT-based marketing strategies among SMEs receive limited support from the government due to the lack of sufficient data to inform policy. This study aims to (1) identify the needs of SMEs for social media promotion training as part of their digital capacity building, (2) develop and compare the effectiveness of classification models that combine Fuzzy C-Means and K-Means clustering algorithms with the Naïve Bayes algorithm to group SMEs based on business characteristics, (3) analyze the relationships between business variables—such as business type, marketing media, funding sources, and financial aspects—and SME performance through regression analysis, and (4) provide data-driven foundations for designing targeted digital interventions and policy strategies to support SME development in Indonesia. This study used UPPKS data from 133 SMEs in seven districts in the Special Region of Yogyakarta. Data analysis covered business types, marketing platforms used, funding sources, and financial performance indicators. Data pre-processing involved cleaning, normalization, and integration to ensure consistency and readiness for analysis. The researcher used the Elbow method to determine the optimal number of clusters. Then, it also used Fuzzy C-Means (FCM) and K-Means to categorize SMEs into three groups: high, medium, and low. The classification was based on the Naïve Bayes algorithm. The evaluation of the model performance used a confusion matrix, cross-validation, and regression analysis to examine inter-variable relationships. The results showed that the combination of FCM and Naïve Bayes achieved an accuracy of 85% based on the confusion matrix and 97% based on cross-validation. Meanwhile, the combination of K-Means and Naïve Bayes respectively achieved an accuracy of 96% and 94.7%. These findings demonstrate the effectiveness of the proposed approaches in classifying SMEs based on their characteristics and performance. This research provides important insights for policymakers and SME development agencies in designing more targeted digital training and support programs. Future studies should explore the integration of other algorithms, such as Support Vector Machines (SVM) and Decision Trees, while incorporating market trends and customer engagement to enhance SME classification and provide ongoing support.
Co-Authors Abdullah, Khairul Hafezad Abdulloh, Ferian Fauzi Acihmah Sidauruk Agastya, I Made Artha Alam Miah, Mohammad Badrul Apitiadi, Satyo Dwi Asadulloh, Bima Pramudya Ashadi, Diki Bety Wulan Sari Bety Wulan Sari, Bety Wulan Dhanar Intan Surya Saputra Diki Ashadi Donni Prabowo, Donni Gunawan, Puguh Hasta Hairani Hairani Hammood, Waleed A. Harianto, Fetrus Jari I Nyoman Switrayana Joang Ipmawati Kusnawi Kusnawi Kusuma, Velizha S Majid Rahardi Mohammad Iskandar Dzulkurnain Mustofa, Dinar Norhikmah, - Wiji Nurastuti, Wiji Wirasto, Anggit Yoga Pristyanto