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All Journal Nuansa Informatika Telematika JUITA : Jurnal Informatika Indonesian Journal on Computing (Indo-JC) JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization Jurnal Informatika Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Mantik Jurnal Teknik Informatika C.I.T. Medicom International Journal of Advances in Data and Information Systems EKONOMI, KEUANGAN, INVESTASI DAN SYARIAH (EKUITAS) Tematik : Jurnal Teknologi Informasi Komunikasi Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian Masyarakat Nusantara Journal of Dinda : Data Science, Information Technology, and Data Analytics Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika AJAD : Jurnal Pengabdian kepada Masyarakat Indonesian Journal of Business Analytics (IJBA) Formosa Journal of Applied Sciences (FJAS) Paradigma Journal Of Information System And Artificial Intelligence Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika International Journal of Computer Technology and Science
Nur Alamsyah
Fakultas Ilmu Komputer PASIM
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Social Sciences

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Perbandingan Model Klasifikasi C4.5, Naïve Bayes, Support Vector Machine dan K-nearest Neighbor untuk Memprediksi Kelayakan Masyarakat dalam Menerima Bantuan PBI APBD Tutik Ultsa Rahmatika; Nur Alamsyah; Titan Parama Yoga; Budiman
TEMATIK Vol. 10 No. 2 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

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Abstract

This research evaluates the eligibility of the community to receive APBD Contribution Assistance (PBI) using four classification algorithms: C4.5, Naïve Bayes, K-Nearest Neighbor, and Support Vector Machine (SVM). There is a problem of inaccurate distribution of assistance, which prompted the selection of these four methods with specific considerations, C4.5 (Decision Tree) is known for its clarity and interpretability, providing an easy-to-understand understanding of the factors that influence classification decisions, Naïve Bayes was selected for its efficiency and speed in training and testing, suitable for large datasets and can be updated quickly with new data, K-Nearest Neighbor (KNN) is used for decision making based on local patterns in the data, useful if the eligibility decision is local or related to the surrounding environment while Support Vector Machine (SVM): Selected for its ability to handle complex and non-linear datasets. The results show that SVM has the highest Weighted Mean Precision, reaching 91.67%, confirming its superiority as the best choice. These findings make a significant contribution to improving the accuracy of determining the eligibility of PBI APBD beneficiaries, supporting targeting accuracy, and ensuring the effectiveness of the assistance program for people in need.
Analisis Perbandingan Sentimen Pengguna Twitter Terhadap Layanan Salah Satu Provider Internet Di Indonesia Menggunakan Metode Klasifikasi Della Puspita Sari; Budiman; Nur Alamsyah
TEMATIK Vol. 10 No. 2 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

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Abstract

The Internet is needed for everyday life, whereas in Indonesia there are many internet service providers, one of which is indihome. Sentiment analysis itself aims to classify a text into Negative, Positive and Neutral classes. On the twitter platform, there are many reviews about internet providers, one of which is indihome, because of poor service or just to appreciate the services provided. Based on the calculation of the results obtained 71.1% negative, 21.1% positive and 7.7% neutral. The data obtained is not balanced, therefore the classification process is assisted using Smote. The results of the comparison of the four methods used are Support Vector Machine, Naïve Bayes, Random forest, Decision tree. From the overall comparison, the highest accuracy without smote or using smote is Support Vector Machine with an accuracy level of 89% AUC level of 89% if using smote gets 93% accuracy and 97% AUC level with 80% training data and 20% testing.
Wi-Fi Fingerprint for Indoor Keyless Entry Systems with Ensemble Learning Regression-Classification Model Aji Gautama Putrada; Nur Alamsyah; Mohamad Nurkamal Fauzan
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: 10.30630/joiv.7.4.01498

Abstract

Keyless entry systems have made human life more comfortable because the possibility of someone leaving a key is non-existent. However, there is a research opportunity to use Wi-Fi fingerprint localization in an indoor keyless entry system. The use of Wi-Fi as a solution is low cost because of the already hdeployed Wi-Fi access points (AP) around buildings. This study uses ensemble learning to utilize Wi-Fi fingerprints for keyless entry systems in indoor environments. The first step of this research is to obtain the UJIIndoorLoc Data Set, an open-source Wi-Fi fingerprint dataset. The next step is to design and implement a keyless entry system based on Wi-Fi Fingerprint. We compared four ensemble learning methods: Random Forest, AdaBoost, gradient boosting, and XGBoost. We use several metrics to compare the four methods, namely r2, the area under the receiver operating curve (AUROC), and the equal error rate (EER). The test results show that for localization based on ensemble regression, XGBoost has the best performance compared to the other three ensemble methods for both longitude and latitude predictions. The r2values are 0.94 and 0.98, respectively. For authentication based on ensemble classification, the gradient-boosting model performance increases as the number of weak learners increases. The optimum number of weak learners is 60. With AUROC = 0.99, gradient boosting outperforms random forest, AdaBoost, and XGBoost on authentication. Finally, our authentication method has EER = 0.01, which outperforms other state-of-the-art keyless entry systems. This research focuses on local building map datasets for future work.
IMPROVING TRAFFIC DENSITY PREDICTION USING LSTM WITH PARAMETRIC ReLU (PReLU) ACTIVATION Nur Alamsyah; Titan Parama Yoga; Budiman Budiman
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5046

Abstract

In the presence of complex traffic flow patterns, this research responds to the challenge by proposing the application of the Long Short-Term Memory (LSTM) model and comparing four different activation functions, namely tanh, ReLU, sigmoid, and PReLU. This research aims to improve the accuracy of traffic flow prediction through LSTM model by finding the best activation function among tanh, relu, sigmoid, and PReLU. The method used starts from the collection of traffic flow datasets covering the period 2015-2017 used to train and evaluate the LSTM model with the four activation functions. Tests were conducted by observing the Train Mean Squared Error (MSE) and Validation MSE. The experimental results show that PReLU provides the best results with a Train MSE of 0.000505 and Validation MSE of 0.000755. Although tanh, ReLU, and sigmoid provided competitive results, PReLU stood out as the optimal choice to improve the adaptability of the model to complex traffic flow patterns.
Eksplorasi Pengaruh Platform dan Gaya Bermain terhadap Tingkat Kecemasan dengan Teknik Algoritmik Nur Alamsyah; Budiman Budiman; Titan Parama Yoga; Reni Nursyanti
SisInfo Vol 6 No 2 (2024): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/sisinfo.v6i2.907

Abstract

Penelitian ini bertujuan mengeksplorasi pengaruh platform dan gaya bermain game terhadap tingkat kecemasan menggunakan teknik algoritmik. Dengan meningkatnya popularitas game digital, memahami faktor-faktor yang mempengaruhi kesejahteraan mental pemain menjadi penting. Data dikumpulkan dari 13,464 responden yang melibatkan berbagai platform dan gaya bermain. Skor kecemasan diukur menggunakan Generalized Anxiety Disorder (GAD) scale, dan analisis statistik dilakukan dengan Analisis Varian (ANOVA) untuk mengidentifikasi perbedaan signifikan dalam skor kecemasan. Hasil menunjukkan bahwa platform game dan gaya bermain memiliki pengaruh signifikan terhadap tingkat kecemasan, sementara jenis game yang dimainkan tidak menunjukkan perbedaan signifikan. Temuan ini mengindikasikan bahwa faktor lingkungan dan interaksi sosial dalam bermain game dapat mempengaruhi tingkat kecemasan pemain lebih dari sekadar jenis game atau durasi bermain. Penelitian ini memberikan wawasan baru mengenai hubungan antara kebiasaan bermain game dan kesejahteraan mental, serta menawarkan landasan untuk penelitian lebih lanjut dalam ilmu komputer dan kesehatan mental.
ACTIVATION FUNCTION IN LSTM FOR IMPROVED FORECASTING OF CLOSING NATURAL GAS STOCK PRICES Budimann Budiman; Nur Alamsyah; R. Yadi Rakhman Alamsyah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5258

Abstract

The closing price of natural gas stocks greatly influences investment decisions and the energy industry. Predicting prices correctly can greatly help investors, market participants, and all parties involved, as it allows for making better decisions and optimizing investment portfolios. By using deep learning methods to role model various LSTM activation functions, such as Sigmoid, ReLU, and Tanh, this exploration will hopefully help understand complex patterns in time series data. By finding an appropriate forecasting method, all parties involved can reduce the environmental impact. The experimental results show that the model with ReLU activation function has the highest R2 value of 0.960 in both the training and test sets, and the model with Tanh activation function is also successful, with R2 values of 0.950 in the training set and 0.949 in the test set, and an MSE of 0.002. The model with the sigmoid activation function was slightly lower, with R2 values of 0.931 in the training set and 0.943 in the test set, and an MSE of 0.003. These findings indicate that the LSTM model with the ReLU activation function is considered better for predicting the closing price of natural gas stocks. These findings may help investors, stakeholders, and market participants choose the most accurate model to predict the closing price of natural gas stocks.
QUIDS: A Novel Edge-Based Botnet Detection with Quantization for IoT Device Pairing Aji Gautama Putrada; Nur Alamsyah; Mohamad Nurkamal Fauzan; Sidik Prabowo; Ikke Dian Oktaviani
Indonesia Journal on Computing (Indo-JC) Vol. 8 No. 3 (2023): December 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2023.8.3.878

Abstract

Advanced machine learning has managed to detect IoT botnets. However, conflicts arise due to complex models and limited device resources. Our research aim is on a quantized intrusion detection system (QUIDS), an edge-based botnet detection for IoT device pairing. Using knearest neighbor (KNN) within QUIDS, we incorporate quantization, random sampling (RS), and feature selection (FS). Initially, we simulated a botnet attack, devised countermeasures via a sequence diagram, and then utilized a Kaggle botnet attack dataset. Our novel approach includes RS, FS, and 16-bit quantization, optimizing each step empirically. The test results show that employing a mean decrease in impurity (MDI) by FS reduces features from 115 to 30. Despite a slight accuracy drop in KNN due to RS, FS, and quantization sustain performance. Testing our model revealed 1200 RS samples as optimal, maintaining performance while reducing features. Quantization to 16-bit doesn’t alter feature value distribution. Implementing QUIDS increased the compression ratio (CR) to 175×, surpassing RS+FS threefold and RS by 13 times. This novel method emerges as the most efficient in CR.
Analisis Sentimen Publik pada Media Sosial Twitter Terhadap Tiket.com Menggunakan Algoritma Klasifikasi Budiman, Budiman; Silvana Anggraeni, Zulmeida; Habibi, Chairul; Alamsyah, Nur
Jurnal Informatika Vol 11, No 1 (2024): April 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i1.17988

Abstract

Analisis sentimen merupakan proses identifikasi emosional seseorang terhadap suatu objek yang akan menghasilkan sentimen positif, negatif dan netral. Kemajuan teknologi ini tentu memberikan pengaruh terhadap berbagai pelaku bisnis untuk saling mengintegrasikan sistem bisnisnya satu sama lain, salah satunya Tiket.com. Hal tersebut tentu menghasilkan sentimen dari masyarakat Indonesia yang diunggah pada platform media sosial Twitter, sehingga membantu individu maupun organisasi dalam mengambil keputusan. Penelitian ini dilakukan untuk mengetahui klasifikasi sentimen masyarakat Indonesia terhadap Tiket.com menggunakan algoritma Naïve Bayes Classifier (NBC), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) dan Random Forest (RF). Berdasarkan perhitungan data sentimen terhadap Tiket.com terdapat 90.3% sentimen positif dan 9.7% sentimen negatif. Persentase tersebut menunjukkan bahwa Tiket.com cukup berpengaruh positif terhadap penggunanya. Berdasarkan hasil pengujian algoritma klasifikasi, diketahui NBC memperoleh tingkat akurasi sebesar 88%, KNN dengan nilai k = 11 mendapatkan akurasi sebesar 91%, SVM menghasilkan tingkat akurasi sebesar 92%, dan tingkat akurasi RF mencapai 93% dengan n_estimators = 100. Kesimpulan pada penelitian ini, Random Forest merupakan algoritma yang memiliki tingkat akurasi paling tinggi dibanding dengan algoritma klasifikasi lain.
CNN Pruning for Edge Computing-Based Corn Disease Detection with a Novel NG-Mean Accuracy Loss Optimization Putrada, Aji Gautama; Oktaviani, Ikke Dian; Fauzan, Mohamad Nurkamal; Alamsyah, Nur
Telematika Vol 17, No 2: August (2024)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v17i2.2899

Abstract

Plant disease detection studies disease attacks in plants detected on the leaves using computer vision. However, some plant disease detection solutions still utilize cloud computing, where the problems include slow processing times and misuse of data privacy. This study aims to evaluate the performance of convolutional neural network (CNN) pruning in edge computing-based plant disease detection. We use Kaggle's plant disease image dataset, which contains three corn diseases. We also created an edge computing system architecture for plant disease detection utilizing the latest communication technology and middleware. Next, we developed an optimal CNN model for plant disease detection using grid search. We pruned the CNN model in the final step and tested its performance. In this step, we developed a novel normalized-geometric mean (NG-mean) method for accuracy loss optimization. The test results show that class weights can optimize specificity and g-mean on the imbalanced dataset, with values of 0.995 and 0.983, respectively. The grid search results then optimize the optimization method's hyperparameters, learning rate, batch size, and epoch to achieve the highest accuracy of 0.947. Applying pruning produces several models with variations in sparsity and scheduling methods. We used the new NG-mean method to find the best compressed model. It had constant scheduling, 0.8 sparsity, a mean accuracy loss of 1.05%, and a CR of 2.71×. This study enhances the efficiency and privacy of plant disease detection by utilizing edge computing and optimizing CNN models, leading to faster processing and better data security. Future work could explore the application of the novel NG-Mean method in other domains and the integration of additional plant species and diseases into the detection system.
XGBOOST HYPERPARAMETER OPTIMIZATION USING RANDOMIZEDSEARCHCV FOR ACCURATE FOREST FIRE DROUGHT CONDITION PREDICTION Alamsyah, Nur; Budiman, Budiman; Yoga, Titan Parama; Alamsyah, R Yadi Rakhman
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5569

Abstract

Climate change and increasing global temperatures have increased the frequency and intensity of forest fires, making fire risk evaluation increasingly important. This study aims to improve the accuracy of predicting forest fuel drought conditions (Drought Code) by using the XGBoost algorithm optimized with RandomizedSearchCV. The research methods include collecting data related to forest fires, preprocessing data to ensure quality and consistency, and using RandomizedSearchCV for XGBoost hyperparameter optimization. The results showed that the optimized XGBoost model resulted in a decrease in Mean Squared Error (MSE) and an increase in R-squared value compared to the default model. The optimized model achieved an MSE of 0.0210 and R2 of 0.9820 on the test data, indicating significantly improved prediction accuracy for forest fuel drought conditions. These findings emphasize the importance of hyperparameter optimization in improving the accuracy of predictive models for forest fire risk assessment.
Co-Authors Acep Hendra Aji Gautama Putrada Akbar, Imannudin Alamsyah, R Yadi Rakhman AlFauzi, Ihsan Arnold Ropen Sinaga Bayu Wibisono, Gunthur Budi Sulistiyo Jati Budiman Danestiara, Venia R Della Octa Irmulvani Devi Silaen Dirham Triyadi Eli Nofita Sari Erpurini, Wala Fauzan, Mohamad Nurkamal Habibi, Chairul Hani Fitria Rahmani Hasan Nuraripin Hermawan, Arief Karditya Ikke Dian Oktaviani Indah Susilawati Ismi Nur Muhamad Januantara Prima, Alif Jennifer Kaunang, Valencia Claudia Kaunang, Valencia Kaunang, Valencia Claudia Jennifer Leonandri, Dino Gustaf Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Muhamad Andriansyah Nurcahya Muhamad Nurkamal Fauzan Muhammad Yusup Iskandar Mutaqin Akbar Nur Imam Febrianto PARAMA YOGA, TITAN Permata Hati, Tiara R. Yadi Rakhman Alamsyah Reni Nursyanti Reni Nursyanti Rijwan Rijwan Rimbun Kencana RIO ANDRIYAT KRISDIAWAN Setiana, Elia Sidik Prabowo Silvana Anggraeni, Zulmeida Syafrial Fachri Pane Tiara Nurhasanah Titan Parama Titan Parama Yoga Tito Sugiharto Tutik Ultsa Rahmatika Valencia Claudia Jennifer Venia Restreva Danestiara Wala Erpurini Wala Erpurini Wia Handayani Yeni