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Alexius Endy Budianto
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INDONESIA
BIMASAKTI
Published by Universitas PGRI Kanjuruhan Malang
ISSN : 23554401     EISSN : 31091652     DOI : https://doi.org/10.21067/bimasakti
Core Subject : Science, Engineering,
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Jurnal Riset Mahasiswa Bidang Teknologi Informasi "BIMASAKTI". Merupakan jurnal ilmiah di bidang ilmu Teknologi Informasi yang berada di bawah naungan Prodi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas PGRI Kanjuruhan Malang (UNIKAMA). Jurnal ini hadir untuk mendorong penyebarluasan pemikiran dan gagasan hasil penelitian tugas akhir mahasiswa di bidang Teknologi Informasi secara luas. Riset Teknologi: Jurnal tersebut dapat fokus pada presentasi hasil penelitian terbaru dalam berbagai bidang teknologi, seperti teknologi informasi, teknologi komunikasi, teknologi material, teknologi energi, teknologi medis, dan sebagainya. Fokus dan Scope Jurnal BIMASAKTI : - Inovasi dan Pengembangan: Menyoroti inovasi baru dan pengembangan teknologi yang memiliki dampak signifikan di berbagai sektor. - Penerapan Teknologi: Mempublikasikan penelitian yang mengeksplorasi penerapan teknologi dalam konteks praktis, seperti implementasi teknologi dalam industri, kesehatan, pendidikan, atau sektor lainnya. - Keamanan Teknologi: Menyoroti aspek keamanan dalam pengembangan dan implementasi teknologi, seperti keamanan informasi, keamanan jaringan, atau keamanan perangkat keras dan lunak. - Kajian Kasus: Menampilkan studi kasus tentang proyek-proyek teknologi tertentu, baik yang sukses maupun yang menghadapi tantangan, untuk memberikan wawasan yang mendalam. - Analisis Tren Teknologi: Menyajikan analisis tren terkini dalam pengembangan teknologi, seperti kecerdasan buatan, Internet of Things (IoT), realitas virtual, dan lainnya. - Aspek Etika dan Sosial: Membahas implikasi etika dan dampak sosial dari perkembangan teknologi, termasuk isu-isu seperti privasi, keamanan data, dan tanggung jawab sosial. - Metodologi Penelitian: Menyoroti metode-metode penelitian yang digunakan dalam mengembangkan teknologi, seperti desain eksperimen, pengembangan prototipe, analisis data, dan sebagainya.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 7 Documents
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Search results for , issue "Vol 8 No 2 (2026): BIMASAKTI" : 7 Documents clear
PENERAPAN METODE NAÏVE BAYES DAN SUPPORT VECTOR MACHINE PADA ANALISIS SENTIMEN NETIZEN DI TWITTER VOLLEY BALL INDONESIA Ginanjar, Wismo; Budianto, Alexius Endy; Ahsan, Moh
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.12376

Abstract

Social media has become an integral part of modern society, offering a platform for public opinion expression. In Indonesia, volleyball is a very popular sport, and Volley Ball Indonesia is the main topic of discussion on social media, especially Twitter. This study aims to analyze the sentiment of netizen comments on the official Twitter account of Volley Ball Indonesia (@volleyball.indonesia) using the Naive Bayes method and Support Vector Machine (SVM). The data used amounted to 2,920 comments from 50 posts in the period of September 28, 2023 - May 10, 2024, focused on the U-23 and Senior Men's National Team matches. Naïve Bayes and SVM were chosen because both are effective methods in sentiment classification. Naïve Bayes uses a probabilistic approach, while SVM looks for the best hyperplane to separate data classes. The results of the study show that both methods can be used to analyze sentiment with a good level of accuracy. The test results on each training data and testing data with different presentations will provide different accuracy results. The test results of the Naive Bayes method obtained the highest accuracy value of 71% with a ratio of 70:30 and the Support Vector Machine obtained the highest accuracy value of 76% with a ratio of 80:20. So it can be concluded that the Support Vector Machine method gets a higher accuracy value than the Naive Bayes method.
PEMILIHAN PROTOKOL VIRTUAL PRIVATE NETWORK MENGGUNAKAN MIKROTIK UNTUK KEBUTUHAN AKSES JARAK JAUH PADA SMK NEGERI 11 MALANG Ramadhanti, Rizka Laela Saputri; Zaini, Akhmad; Nugraha, Danang Aditya
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.12615

Abstract

State Vocational School 11 Malang is a school that uses technology and information. Administrators have the duty to monitor and understand the condition of the school network. The problem when the administrator is outside the local network or public network. The administrator needs remote access to access the server or application safely without data leakage. VPN provides security by encrypting internet traffic. PPTP, L2TP, OpenVPN are VPN protocol options that can be used to connect between different networks. In this research, testing was carried out between these protocols to find out which one has the best performance and the ability to maintain the confidentiality of the data and information stored in it.
PENERAPAN ALGORITMA C4.5 PADA ULASAN APLIKASI SHOPEE DI GOOGLE PLAY STORE Andriasih, Indra Fitri; Budianto, Alexius Endy; Walidaroyani, Ainia
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.12763

Abstract

Shopee, as one of the most popular e-commerce platforms in Indonesia, receives numerous user reviews on the Google Play Store. These reviews contain valuable information that can be leveraged to evaluate the quality of the application’s services. This study aims to classify user sentiment using the C4.5 algorithm to assist developers in better understanding user perceptions. The data were collected from the Google Play Store and processed through several stages, including preprocessing (case folding, stopword removal, stemming, word normalization, and sentiment labeling), data transformation using the TF-IDF method, and splitting the dataset into training and testing sets. The C4.5 algorithm was implemented using the DecisionTreeClassifier model with entropy as the criterion. The results indicate that the classification model achieved an accuracy of 83.25% on the test data. The model demonstrated strong performance in classifying positive sentiment, while the classification of negative and neutral sentiments was less optimal due to class imbalance. Therefore, the C4.5 algorithm proves to be effective in classifying user review sentiment, particularly in identifying positive sentiment. These findings can serve as valuable input for Shopee's developers to improve their services based on user feedback.
ANALISIS OPINI FILM PADA NETFLIX DENGAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE MENGGUNAKAN SELEKSI FITUR CHI-SQUARE Riady, Rahman; Budianto, Alexius Endy; Ahsan, Moh
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.12982

Abstract

This research aims to analyse user opinions on films on the Netflix platform using the Naïve Bayes algorithm and Support Vector Machine. The focus of the research is to increase classification accuracy through feature selection using the Chi-square method. The data used is obtained through a web scraping process of user reviews on Google Play Store. Automatic labeling is supported by the Transformers library, resulting in 131 positive labels and 869 negative labels from 1000 reviews. The research stages include data crawling, automatic labeling using the Transformers library, pre-processing (case folding, tokenisation, stopword removal, normalisation, and stemming), weighting with the TF-IDF method, and testing model accuracy using data split ratios of 90:10, 80:20, and 70:30. The findings of the study indicate that the Support Vector Machine algorithm reached an accuracy rate of 92.5% using the 80:20 data split, whereas its Chi-square enhanced variant attained 91.5% accuracy on the same dataset. Meanwhile, the Naïve Bayes classifier recorded an accuracy of 82%, and its Chi-square integrated version yielded 79%. These results suggest that incorporating Chi-square did not enhance the predictive performance of either the Naïve Bayes or Support Vector Machine approaches in this research.
OPTIMASI RANDOM FOREST TERHADAP DATA PENYAKIT LIVER MENGGUNAKAN FIREFLYALGORITHM Sunarjo, Nemesius; Nugraha, Danang Aditya; Santoso, Heri
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.13041

Abstract

Liver disease is one of the most dangerous diseases for human survival. In an effort to find out liver disease early on, a classification method is needed. Researchers conducted testing and classification of lliver disease with the Random Forest algorithm which was then optimized with the Firefly algorithm. The purpose of this study is to learn how the application of the firefly algorithm in optimizing the accuracy of the random forest algorithm in liver disease. The data used is 1700 data with 11 attributes. The findings of this study with the Random Forest algorithm produced an accuracy of 87.24% while when optimized using the Firefly Algorithm produced an accuracy of 93.24%. The findings demonstrated a rise in the precision of the Random Forest algorithm and optimized using Firefly Algorithm.
PERBANDINGAN METODE ARTIFICIAL NEURAL NETWORK, DAN RANDOM FOREST PADA KLASIFIKASI TINGKAT OBESITAS Agung Indra; Amak Yunus; Budianto, Alexius Endy
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.13171

Abstract

Classification of obesity levels is an important step in supporting efforts to tackle the increasing prevalence of obesity. This study aims to compare the performance of machine learning methods, namely Artificial Neural Network (ANN) and Random Forest (RF), in classifying obesity levels based on a predetermined dataset. The research method involved data preprocessing and model training with varying proportions of training and testing data (70:30, 80:20, and 90:10). The results showed that Random Forest provided higher accuracy than Artificial Neural Network. In testing with 70% training data and 30% testing data, ANN produced an accuracy of 88.20% while RF reached 97.28%. With a training data proportion of 80% and testing data of 20%, the accuracy of ANN increased to 88.76%, while RF produced 97.37%. With a training data proportion of 90% and testing data of 10%, ANN achieved the highest accuracy of 91.39%, but it was still lower than RF, which reached 95.69%. Based on these results, it can be concluded that the Random Forest algorithm shows more optimal performance than Artificial Neural Network in obesity level classification.
PENERAPAN ALGORITMA LOGISTIC REGRESSION UNTUK KLASIFIKASI PENYAKIT STROKE Amelia, Rachel Trivica; Nugraha, Danang Aditya; Ahsan, Moh
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.13201

Abstract

Stroke is one of the leading causes of death worldwide, ranking after heart disease and cancer. Early detection of stroke risk is essential to enable faster and more accurate treatment. The purpose of this study is to apply the Logistic Regression algorithm to classify stroke cases based on several risk factors, including gender, age, hypertension, heart disease, marital status, occupation, residence type, average glucose level, body mass index (BMI), smoking status, and stroke status. The dataset used in this research was obtained from Kaggle and consists of 5,110 patient records. The research process involves several stages, including data cleaning, data transformation, and normalization using the Min-Max Scaler method, followed by splitting the data into training and testing sets with various proportions (90%-10%, 85%-15%, 80%-20%, 70%-30%, and 65%-35%). The evaluation was conducted using a Confusion Matrix with performance metrics such as accuracy, precision, recall, and F1-score. The analysis results show that the 90%-10% data split achieved the highest accuracy of 76.17%, with precision and recall values indicating that the model performs well in identifying non-stroke cases. However, performance on the minority class (stroke) remains relatively low, suggesting the need for improvement through data imbalance handling. Overall, the application of the Logistic Regression algorithm proved to be effective for initial stroke classification, although accuracy can still be improved through resampling techniques or advanced model optimization.

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