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Collaboration FMADM And K-Means Clustering To Determine The Activity Proposal In Operational Management Activity Awangga, Rolly Maulana; Pane, Syafrial Fachri; Tunnisa, Khaera
EMITTER International Journal of Engineering Technology Vol 7, No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Indonesian government agencies under the Ministry of Energy and Mineral Resources still use manual methods in determining and selecting proposals for operational activities to be carried out. This study uses the Decision Support System (DSS) method, namely Fuzzy Multiple Attribute Decision Decision (Fmadm) and K-Means Clustering method in managing Operational Plan activities. Fmadm to select the best alternative from a number of alternatives, alternatives from this study proposed activity proposals, then ranking to determine the optimal alternative. The K-Means Clustering Method to obtain cluster values for alternatives on the criteria for activity dates, types of activities, and activity ceilings. The last iteration of the Euclidian distance calculation data on k-means shows that alternatives that have the smallest centroid value are important proposal criteria and the largest centroid value is an insignificant proposal criteria. The results of the collaboration of the Fmadm and K-Means Clustering methods show the optimal ranking of activities (proposal activities) and the centroid value of each alternative.

Prediksi Jumlah Penjualan Rumah di Bojongsoang ditengah Pandemi Covid-19 dengan Metode ARIMA Kurniawan, Alit Fajar; Pane, Syafrial Fachri; Awangga, Rolly Maulana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3121

This study aims to determine the accuracy of the ARIMA method with the Carmer matrix in forecasting or predicting the number of house sales in the Bojongsoang area which is still experiencing a period of crisis. The data used in this study is secondary data in the form of time series data on the number of house sales. In the ARIMA method, we perform stationary data, then look for autoregressive (AR), moving average (MA), and ARMA (Autoregressive and Moving Average) values. From the available data, the number of house sales has decreased, therefore forecasting is carried out using the ARIMA (1,1,1) model for future home sales to assist property developers in estimating future development projects. the results of the forecasting carried out using the ARIMA (1,1,1) method, which shows that the prediction of the number of house sales in the Bojongsoang area in the June - December period experienced a stable number of house sales

Meningkatkan Akurasi Long-Short Term Memory (LSTM) pada Analisis Sentimen Vaksin Covid-19 di Twitter dengan Glove Poetra, Chandra Kirana; Pane, Syafrial Fachri; Fatonah, Nuraini Siti
Jurnal Telematika Vol. 16 No. 2 (2021)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v16i2.400

Covid-19 began to appear in early 2020. The spread of this outbreak is often discussed on Twitter, especially about vaccine procurement. For this reason, it is necessary to have a sentiment analysis on the opinion on vaccine procurement. Sentiment analysis will use the Long Short Term Memory (LSTM) method. However, the level of accuracy of LSTM itself is not accurate enough compared to another method, such as Bi-LSTM. Therefore, it is necessary to optimize so that the LSTM model can predict accurately and compete with the accuracy of Bi-LSTM. Optimization is done by using the Glove method. The Glove method works by counting the occurrences of one word with another and then converting it to a vector. Words that often appear together will have vector values that are close to each other. This vector value is then used as a reference and inserted into the embedding layer of the LSTM model. The application of LSTM coupled with the Glove method resulted in an accuracy of 89% (87% for LSTM and 88% for Bi-LSTM). In this study, the Glove method could increase the accuracy of the used model by 2%. Covid-19 mulai muncul di awal tahun 2020. Penyebaran wabah ini sering dibicarakan di Twitter, terutama tentang pengadaan vaksin. Untuk itu, perlu adanya analisis sentimen terhadap opini pengadaan vaksin. Analisis sentimen akan menggunakan metode Long Short Term Memory (LSTM). Namun, tingkat akurasi LSTM sendiri belum cukup akurat dibandingkan dengan metode lainnya, seperti Bi-LSTM. Oleh karena itu, perlu dilakukan optimalisasi agar model LSTM dapat memprediksi secara akurat dan dapat menyaingi akurasi Bi-LSTM. Optimalisasi dilakukan dengan menggunakan metode Glove. Metode Glove bekerja dengan menghitung kemunculan satu kata dengan kata lainnya lalu mengonversinya menjadi vektor. Kata yang sering muncul secara bersamaan akan memiliki nilai vektor yang saling mendekati. Nilai vektor ini kemudian dijadikan referensi dan dimasukkan ke lapisan embedding pada model LSTM. Penerapan LSTM yang ditambah dengan metode Glove menghasilkan akurasi sebesar 89% (87% untuk LSTM dan 88% untuk Bi-LSTM). Dalam penelitian ini penerapan metode Glove dapat meningkatkan akurasi model sebesar 2%.

Menentukan Faktor-Faktor Akademik yang Mempengaruhi Hasil Belajar Online Selama Pandemi COVID-19 Pane, Syafrial Fachri; Fajri, Ravi Rahmatul
Jurnal Eksplora Informatika Vol 12 No 1 (2022): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v12i1.789

Pandemi COVID19 adalah krisis kesehatan global. Dalam bidang pendidikan, pembelajaran online dengan sistem e-learning merupakan kebutuhan yang tidak tergantikan. Banyak yang berpendapat bahwa pembelajaran online adalah krisis pendidikan saat ini. Namun, sebagian besar siswa tidak tertarik untuk belajar online karena mengandalkan kualitas Internet, yang membatasi interaksi mereka dan membuat kualitas suara dan gambar tidak stabil. Tentu tidak mudah untuk mengetahui faktor akademik yang mempengaruhi hasil belajar online selama pandemi COVID-19. Oleh karena itu, penelitian ini bertujuan untuk menentukan faktor-faktor akademik yang mempengaruhi hasil belajar online selama pandemi COVID-19. Menggunakan data lokal Politeknik di Pulau Jawa. Penelitian ini menggunakan analisis Cronbach-Alpha, Bayesian Exploration, EFA-tradisional dan Analisis Regresi Multivariat (OLS). Hasil evaluasi skala penelitian menunjukkan bahwa 28 variabel diamati. Hasil uji hipotesis menunjukkan bahwa hasil belajar online dipengaruhi oleh enam faktor. Desain kursus, kegunaan yang dirasakan, kemudahan penggunaan, Karakteristik pembelajaran, Kapasitas fakultas, Konten kursus. Regresi multivariat berdasarkan metode kuadrat minimum (OLS) untuk mengevaluasi faktor-faktor spesifik yang mempengaruhi pembelajaran online dan menguji hipotesis. Tingkat akurasi model OLS sebesar 45,8%.

Pemodelan Arsip Novel Berdasarkan Klaster Data dan Penyaringan Awangga, Rolly Maulana; Pane, Syafrial Fachri; Kurniawan, Cahya
Technomedia Journal Vol 4 No 2 Februari (2020): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

File archiving now needs to be appropriately managed so that it is easy to find and manage. File archiving in question is how to help in the process of finding data with a considerable number, to facilitate the work following the aim to reduce the time the search data can be integrated with the system created. Archiving itself aims to facilitate the management of data that is very diverse and with a large amount, to facilitate the management and also the control carried out. The problem with filing archives, in this case, is the lack of management regarding the correct filing of archives. Controlled archive file management makes the amount of time that is passed only by searching files. By looking at large amounts of data, it is necessary to use methods to be able to manage and search, with Alphabet, Numeric and K-means Clustering methods designed and processed, it is easier to manage existing data searches so that the work doesn't take too much time. There needs to be further development of the analysis carried out at this time to further improve the effectiveness by creating a system following the analysis rules made at this time. Keywords: Filing, Archives, File Management Alphabetical Filling System, Numerical Filing System, K-means Clustering.

Pengaruh Hyperparameter Tuning untuk Efektivitas pada Pendekatan Hybrid dalam Mendiagnosis Stres dan Depresi : Tinjauan Studi Literatur Ramadhan, Bachtiar; Pane, Syafrial Fachri
Jurnal Tekno Insentif Vol 18 No 2 (2024): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36787/jti.v18i2.1516

Tinjauan Sistematik Literatur ini mengkaji efektivitas hyperparameter tuning dalam pendekatan hybrid untuk diagnosis kesehatan mental, menggunakan metode PRISMA untuk evaluasi model prediksi. Penelitian menunjukkan bahwa masih jarang studi yang mengintegrasikan machine learning dengan tuning untuk mengidentifikasi variabel krusial dalam diagnosis kesehatan mental. Tujuan utama adalah menganalisis variabel penting, model yang sering digunakan, dan metode tuning terbaik. Hasil menunjukkan bahwa usia dan jenis kelamin adalah variabel kunci, dengan Random Forest dan Tree-Structured Parzen Estimator dengan Gradient Boosting sebagai model tuning terbaik, mencapai akurasi 0.986. Penelitian ini menyarankan penggunaan genetic algorithm untuk meningkatkan efisiensi dan mengatasi masalah overfitting dan underfitting, serta mendorong eksplorasi lebih lanjut pada kombinasi model dalam kesehatan mental.

DETEKSI EMOSI PADA TEKS BERBAHASA INDONESIA MENGGUNAKAN PENDEKATAN ENSEMBLE Pane, Syafrial Fachri; Abdullah, Faisal; Habibi, Roni
Jurnal Teknologi Terapan Vol 10, No 2 (2024): Jurnal Teknologi Terapan
Publisher : P3M Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/jtt.v10i2.551

Emotion in written text is often difficult to recognize due to the absence of visual cues such as facial expressions or vocal intonation, which typically aid in understanding a person's feelings. This research aims to address this challenge by developing an emotion detection model for Indonesian text. The approach used is Ensemble Learning, combining three Machine Learning models: SVM, KNN, and XGBoost, to optimize emotion detection results. The main contribution of this research is the implementation of the Ensemble method for detecting emotions in Indonesian text, with performance evaluated using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. The evaluation results show that the Ensemble model outperforms previous models, achieving an accuracy, precision, recall, and F1 score of 87.14%, and a ROC AUC score of 97.90%. To further enhance performance, this study utilizes GridSearchCV for hyperparameter tuning of the SVM and XGBoost models and employs the Automated Machine Learning (AutoML) tool TPOT to generate the KNN model.

Enhancing Prediction Accuracy of the Happiness Index Using Multi-Estimator Stacking Regressor and Web Application Integration Zain, Rofi Nafiis; Harani, Nisa Hanum; Pane, Syafrial Fachri
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.1871

This study proposes a novel approach to enhance the prediction accuracy of the Happiness Index using a multi-estimator stacking regressor model and web application integration. By combining diverse regression models, such as decision tree, random forest, gradient boosting, LGBM, and support vector regressor (SVR), the proposed ensemble architecture achieved superior predictive performance with an score of 0.9814. A custom Happiness Score was formulated using weighted indicators derived from Pearson’s correlation analysis. Furthermore, SHapley Additive exPlanations (SHAP) were used to interpret model predictions, revealing the Human Development Index, Female Labour Force Rate, and Life Expectancy as key contributing features. The final model was deployed via a Python Flask-based web dashboard, enabling stakeholders to visualize happiness metrics interactively. The results suggest that stacking-based regression, when combined with interpretability techniques and real-time deployment, can offer a powerful solution for socioeconomic modeling and supporting urban policy.

Security Analysis of Two-Factor Authentication Applications: Vulnerabilities in Data Storage and Management Pane, Syafrial Fachri; Haq, Dzikri Izzatul; Siregar, M. Amran Hakim
Mobile and Forensics Vol. 7 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i2.13312

In the digital era, two-factor authentication (2FA) is used as an additional security measure to protect user access to digital services. However, the storage methods of authentication data in 2FA applications have potential security vulnerabilities that can be exploited. This study analyzes five popular 2FA applications, namely Google Authenticator, 2FAS, Aegis Authenticator, Okta Verify, and TOTP Authenticator, focusing on how secret keys are stored and the potential exploitation risks. The experiment was conducted in a virtual Android environment using rooted BlueStacks 5. Data acquisition was performed using Media Manager and X-Plore File Manager, followed by data analysis with SQLite Browser and bypass with PyOTP. The results indicate that some applications store secret keys in plaintext or weak encryption, making authentication bypass possible by manually generating OTP codes. This study concludes that strengthening data storage security in 2FA applications is crucial to prevent unauthorized exploitation by malicious actors.

Menentukan Faktor-Faktor Akademik yang Mempengaruhi Hasil Belajar Online Selama Pandemi COVID-19 Pane, Syafrial Fachri; Fajri, Ravi Rahmatul
Eksplora Informatika Vol 12 No 1 (2022): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v12i1.789

Pandemi COVID19 adalah krisis kesehatan global. Dalam bidang pendidikan, pembelajaran online dengan sistem e-learning merupakan kebutuhan yang tidak tergantikan. Banyak yang berpendapat bahwa pembelajaran online adalah krisis pendidikan saat ini. Namun, sebagian besar siswa tidak tertarik untuk belajar online karena mengandalkan kualitas Internet, yang membatasi interaksi mereka dan membuat kualitas suara dan gambar tidak stabil. Tentu tidak mudah untuk mengetahui faktor akademik yang mempengaruhi hasil belajar online selama pandemi COVID-19. Oleh karena itu, penelitian ini bertujuan untuk menentukan faktor-faktor akademik yang mempengaruhi hasil belajar online selama pandemi COVID-19. Menggunakan data lokal Politeknik di Pulau Jawa. Penelitian ini menggunakan analisis Cronbach-Alpha, Bayesian Exploration, EFA-tradisional dan Analisis Regresi Multivariat (OLS). Hasil evaluasi skala penelitian menunjukkan bahwa 28 variabel diamati. Hasil uji hipotesis menunjukkan bahwa hasil belajar online dipengaruhi oleh enam faktor. Desain kursus, kegunaan yang dirasakan, kemudahan penggunaan, Karakteristik pembelajaran, Kapasitas fakultas, Konten kursus. Regresi multivariat berdasarkan metode kuadrat minimum (OLS) untuk mengevaluasi faktor-faktor spesifik yang mempengaruhi pembelajaran online dan menguji hipotesis. Tingkat akurasi model OLS sebesar 45,8%.

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