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All Journal International Journal of Evaluation and Research in Education (IJERE) Jurnal Kependidikan: Penelitian Inovasi Pembelajaran Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science Cyberspace: Jurnal Pendidikan Teknologi Informasi INOVTEK Polbeng - Seri Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL PENDIDIKAN TAMBUSAI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Antivirus : Jurnal Ilmiah Teknik Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer JUKANTI (Jurnal Pendidikan Teknologi Informasi) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Mnemonic INFORMASI (Jurnal Informatika dan Sistem Informasi) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Abdimasku : Jurnal Pengabdian Masyarakat Aiti: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Komunikasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Business and Audit Information System (JBASE) Jurnal Indonesia : Manajemen Informatika dan Komunikasi DECODE: Jurnal Pendidikan Teknologi Informasi Jurnal Minfo Polgan (JMP) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Eduvest - Journal of Universal Studies SmartComp Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Evangs Mailoa
Universitas Kristen Satya Wacana
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences Other

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Prediksi Penyakit Getah Bening dengan Algoritma Linear Regresi Berganda Dondan, Christofael Natalio; Mailoa, Evangs
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6277

Abstract

This study aims to evaluate the performance of the multiple linear regression algorithm in predicting lymph node diseases by utilizing a multivariate dataset. This algorithm was chosen for its ability to analyze complex relationships between independent and dependent variables, which is expected to provide accurate prediction results. The model evaluation was conducted using three key metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE), to measure prediction error levels and model reliability. The study results indicate that the multiple linear regression algorithm achieved MAE of 0.3, MSE of 0.3, and RMSE of 0.5. These values demonstrate low prediction error and acceptable accuracy, suggesting the algorithm's potential for application in assisting the diagnosis of lymph node diseases.
Metaheuristics Approach for Hyperparameter Tuning of Convolutional Neural Network Purnomo, Hindriyanto; Tad Gonsalves; Evangs Mailoa; Santoso, Fian Julio; Pribadi, Muhammad Rizky
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 3 (2024): June 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i3.5730

Abstract

Deep learning is an artificial intelligence technique that has been used for various tasks. Deep learning performance is determined by its hyperparameter, architecture, and training (connection weight and bias). Finding the right combination of these aspects is very challenging. Convolution neural networks (CNN) is a deep learning method that is commonly used for image classification. It has many hyperparameters; therefore, tuning its hyperparameter is difficult. In this research, a metaheuristic approach is proposed to optimize the hyperparameter of convolution neural networks. Three metaheuristic methods are used in this research: ant colony optimization (ACO), genetic algorithm (GA), and Harmony Search (HS). The metaheuristics methods are used to find the best combination of 8 hyperparameters with 8 options each which creates 1.6. 107 of solution space. The solution space is too large to explore using manual tuning. The Metaheuristics method will bring benefits in terms of finding solutions in the search space more effectively and efficiently. The performance of the metaheuristic methods is evaluated using MNIST datasets. The experiment results show that the accuracy of ACO, GA and HS are 99,7%, 97.7% and 89,9% respectively. The computational times for the ACO, GA and HS algorithms are 27.9 s, 22.3 s, and 56.4 s, respectively. It shows that ACO performs the best among the three algorithms in terms of accuracy, however, its computational time is slightly longer than GA. The results of the experiment reveal that the metaheuristic approach is promising for the hyperparameter tuning of CNN. Future research can be directed toward solving larger problems or improving the metaheuristics operator to improve its performance.
Analisa Tweet Mahasiswa untuk Deteksi Gejala Depresi dengan Penerapan Natural Language Processing Dhinora, Monica Yoshe; Mailoa, Evangs
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 2 (2025): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i2.1405

Abstract

Mental health issues are increasingly gaining attention, with depression being a primary factor linked to high suicide rates caused by psychological disorders. College students are identified as a vulnerable group to depression and anxiety, which can be triggered by various factors. Meanwhile, individuals self-expression on social media, especially on platform X (Twitter), which offers freedom of expression, is considered reflective of one’s mental well-being. This study aims to explore the analysis of college students tweets using a Natural Language Processing (NLP) approach to detect depressive symptoms through linguistic patterns. Data was collected via crawling techniques using keywords such as “depression”, “stress”, and “burnout” resulting in 24,167 tweets from January to March 2025. After data cleaning, 8,308 tweets remained. Sentiment labeling using the Inset Lexicon shows that 68.1% (5,663 tweets) were labeled negative, reflecting college students tendency to use platform X as a medium to express negative emotions. The Random Forest model integrated with TF-IDF feature extraction achieved 87.51% accuracy, demonstrating its capability to address majority class bias (negative) and capture the morphological complexity of informal language. The implications of the research encourage the development of a digital monitoring system for the proactive detection of college college students depression symptoms. The lexicon’s limitations in incorporating informal vocabulary (slang) becomes a recommendation for further research to enhance analysis accuracy.
ANALISIS SENTIMEN ULASAN KONSUMEN MENGGUNAKAN ALGORITMA TF-ID UNTUK MENGETAHUI TINGKAT KEPUASAN PELANGGAN(STUDI KASUS : GUNTHEM PREMIUM COFFEE) Widyastuti, Fransisca Sonia; Mailoa, Evangs
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 16 No. 2 (2025): September
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v16i2.1010

Abstract

In today’s digital era, customer review shared across online platforms are regarded as key indicators for evaluating customer satisfaction and shaping the reputation of s business, including coffee shops. In this study, sentiment analysis was conducted on customer reviews of Gunthem Premium Coffee using the TF-IDF (Term Frequency – Inverse Documen Frequency) method. A total of 46 review entries were collected from Google Maps and GoFood and were manually labeled as either positive or negative. The analysis was carried out in several stages, including text pre-processing, manual labeling, and feature extraction using TF-IDF. Irrelevant word were removed, and important terms were identified based on their weight across the dataset. The result showed that most reviews expressed positive sentiments, with words such as “delicious”, “coffee”, “comfortable”, and “clean” found to have the highest TF-IDF weights. A wordcloud visualization was also created to support the analytical findings. Therefore, the TF-IDF method was proven effective in identifying customer opinions and can serve as a foundation for formulating strategies to enchance service quality and customer satisfaction in the coffee shop industry..
MARKET BASKET ANALYSIS MENGGUNAKAN ALGORITMA APRIORI UNTUK MENDUKUNG STRATEGI PROMOSI PRODUK Bintang Samasto, Revo; Mailoa, Evangs
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 16 No. 2 (2025): September
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v16i2.1011

Abstract

The intense business competition requires companies to deeply understand consumer behavior in order to design effective marketing strategies. This study was conducted to analyze customer purchasing patterns using the market basket analysis method with the Apriori algorithm. This was done because previously the company carried out promotional strategies conventionally without utilizing data analysis or understanding consumer purchasing patterns, resulting in less optimal promotional outcomes. The data used consists of 103 briquette sales transactions during the period from March 2024 to March 2025, which were then processed to find frequent itemsets and association rules. The analysis results show that the combination of Hexagonal With Hole (non-premium) briquettes and Rectangle (non-premium) briquettes is the most frequently purchased together, with a support value of 39.81%, a confidence value of 91.11%, and a lift value of 1.42. These findings provide strategic insights that companies can use to design promotions through bundling and cross-selling.
PENGELOMPOKAN GAYA BELAJAR SISWA UNTUK MENDUKUNG EFEKTIFITAS PEMBELAJARAN DENGAN K-MEANS Krisna Setiawan; Mailoa, Evangs
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 16 No. 2 (2025): September
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v16i2.1013

Abstract

Many schools use learning styles that are not aligned with students' preferences, which can hinder the effectiveness of learning. This study aims to cluster students based on their learning style preferences to support more effective teaching. Data were collected from 27 students using a questionnaire covering four learning styles: Visual, Auditory, Reading/Writing, and Kinesthetic. The K-Means algorithm was used to cluster the data, resulting in three optimal clusters with the highest Silhouette Score of 0.340. The clustering results show three student groups: Visual, Auditory-Reading, and Kinesthetic, with the highest average academic scores in the Auditory-Reading cluster. This study demonstrates that clustering based on learning styles can improve academic achievement, and it is recommended to implement personalized teaching strategies according to students' learning style clusters to enhance learning effectiveness.
Pengembangan Aplikasi Web Manajemen Iklan Radio dan Videotron di Kota Salatiga menggunakan MERN stack Putri, Tiara Syah Indra; Mailoa, Evangs
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6496

Abstract

Iklan radio dan videotron memainkan peran penting dalam periklanan modern, termasuk di Kota Salatiga, di mana kedua media ini turut berkontribusi dalam membentuk identitas kota dan mendukung strategi bisnis. Seiring dengan meningkatnya penggunaan media ini, muncul permasalahan dalam manajemen data iklan yang dilakukan secara manual menggunakan spreadsheet dan pencatatan manual, yang mengakibatkan ketidaksesuaian data. Untuk mengatasi masalah ini, penelitian ini bertujuan untuk mengembangkan aplikasi web manajemen iklan radio dan videotron menggunakan teknologi MERN Stack, yang terdiri dari MongoDB, ExpressJS, ReactJS, dan NodeJS. MERN Stack dipilih karena kemampuannya dalam membangun aplikasi dinamis dan skalabel dengan menggunakan JavaScript. Dengan menerapkan arsitektur MVC (Model, View, Controller), aplikasi ini diharapkan dapat meningkatkan efisiensi pengelolaan data iklan, mengotomatisasi proses manajemen, dan memperbaiki efektivitas sistem yang ada. Penelitian ini juga meninjau relevansi penggunaan MERN Stack dalam konteks aplikasi manajemen data lainnya dan membandingkannya dengan pendekatan yang telah ada sebelumnya
Co-Authors Ade Iriani Ade Kresna Dewantara Adeline Debby Christiana Adilla, Denise Sheryl Adinda Setya Oktami Adji, Leonard Surya Adji Antonia .M, Hutami Jane Anugramidah Limbong Madika Arbitra Satria Perdana Arief Wijanarko Azzahra, Windy Livia Bernardus Redika Westama Putra Bintang Samasto, Revo Budhi Kristianto Candrawira Wicaksana, Gede Abdi Danny Manongga Dewi, Teresia Ardika Dhinora, Monica Yoshe Dondan, Christofael Natalio Dwi Hosanna Bangkalang Epliani Limbong Rara Fairiani, Ayuquinn Astuticein Faqih, Muhammad Faiq Adhitya Fredrik Landjamara Ndjurumana G.A, Ambrosius Ludang Gede Abdi Candrawira Wicaksana Gilang Agung Saputra Harnanda, Elifas Gavra Hartono, Michael Antonius Harun B S O. Mosioi Hendry Hindriyanto Dwi Purnomo Irwan Sembiring Jacob Daan Engel Joseph Triwin Subarjo Juan Rondor Jonathan Sumakul Krisetianto, Wijaya Yoga Krisna Setiawan Luhukay, Priscila Karen Madika, Fiwi Fishinsky Muhammad Arifin Muhammad Rizky Pribadi Natacia, Fanny Nawawi, Arif Hasan Nina Setiyawati Panca Rizki Perkasa Perkasa, Panca Rizki Pramastya, Pragnanta Yopie Putri, Tiara Syah Indra Raynaldo Pakan Santoso, Fian Julio Saputra, Krisna Adi Sugeng Ariyadi, Faisal Nuryawan Susanti, Era Syahreza Triadhana Marsudi Tad Gonsalves Teresia Ardika Dewi Tesalonika, Ribka Tetikay, Aprillia Stefvani Ventje Jeremias Lewi Engel Widyasari, Elvira Resti Widyastuti, Fransisca Sonia