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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 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) Jurnal Pengabdian Papua 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 Humanities 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 Medicine & Pharmacology Neuroscience Social Sciences Other

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Analisis User Interface dan User Experience Pada Aplikasi SinauWae Menggunakan Metode Heuristic Evaluation Syahreza Triadhana Marsudi; Evangs Mailoa
INFORMASI (Jurnal Informatika dan Sistem Informasi) Vol 16 No 2 (2024): INFORMASI (Jurnal Informatika dan Sistem Informasi)
Publisher : LPPM STMIK Indonesia Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37424/informasi.v16i2.315

Abstract

SinauWae adalah aplikasi platform pembelajaran online yang terintegrasi dirancang untuk mendukung guru dan siswa dalam melaksanakan kegiatan belajar mengajar yang efektif sesuai jenjang pendidikan SMP dan SMA. Aplikasi SinauWae merupakan aplikasi baru dan belum pernah ada yang melakukan pengujian tingkat kepuasan dan kenyamanan penggunanya. Hal tersebut mendorong perlu dilakukannya analisis pengalaman dalam menggunakan aplikasi SinauWae dengan metode Heuristic Evaluation. Heuristic Evaluation merupakan metode pengukuran yang mengevaluasi pengalaman dan kenyamanan pengguna dengan menerapkan 10 prinsip heuristic. Hasil pengolahan data kuesioner menggunakan metode Heuristic Evaluation menghasilkan kesimpulan adanya 5 urgensi permasalahan dengan tingkatan mayor yang terdapat pada aplikasi SinauWae dan masih perlu adanya perbaikan pada desain serta fitur agar bisa meningkatkan pengalaman pengguna.
ALMUSTRA: An Augmented reality application for introducing traditional musical instruments Mailoa, Evangs; Widyasari, Elvira Resti
Jurnal Kependidikan: Penelitian Inovasi Pembelajaran Vol 8, No 2 (2024)
Publisher : Directorate of Research and Community ServiceUniversitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jk.v8i2.72415

Abstract

Traditional Indonesian musical instruments reflect the richness and diversity of culture and are an integral part of people's lives. However, in recent decades, it has faced challenges in preserving and introducing it to the younger generation. Arts and Culture teachers in Junior High School at Salatiga face difficulties in showing examples of the shapes and sounds of traditional musical instruments from various regions in Indonesia. Augmented reality (AR) technology is emerging as an innovative tool to introduce and preserve local knowledge about traditional musical instruments. This research uses a mixed-method approach which combines qualitative and quantitative data collection. Data collection was carried out through semi-structured interviews with junior high school arts and culture teachers. For system development, the Multimedia Development Life Cycle (MDLC) method is used with the aim of producing multimedia learning products. This research resulted in an Android application called ALMUSTRA: Introduction to Traditional Musical Instruments Based on Augmented Reality. ALMUSTRA is a learning product that helps introduce Indonesian traditional musical instruments, equipped with 3D images, sounds, and quizzes as gamification to make the teaching and learning process more interesting.
Clustering zonasi daerah rawan bencana alam Provinsi Jawa Tengah menggunakan algoritma k-means dan library geopandas Faqih, Muhammad Faiq Adhitya; Mailoa, Evangs
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 1 (2025): IT-Explore Februari 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i1.2025.pp116-127

Abstract

Based on the 2016-2020 Central Java Disaster Risk Assessment, floods and landslides are the most frequent disasters, with 818 flood cases accounting for 31.33% of the total disasters and landslides accounting for 29.57%. This study aims to cluster disaster-prone areas in Central Java using the K-Means algorithm and the GeoPandas library. Data on disaster events for the period 2019-2023 was obtained from the National Disaster Management Agency, while administrative map data of Central Java was downloaded from the Geoportal of Central Java Province. The research stages include data collection, data cleaning, standardization using the Standard Scaler method, application of the K-Means algorithm for regional clustering, and visualization of results using GeoPandas. The results showed that Central Java was divided into four clusters, namely: cluster 0 (disaster-prone areas) includes 3 regions, cluster 1 (non-disaster-prone areas) has 22 regions, cluster 2 (flood-prone areas) consists of 7 regions, and cluster 3 (landslide-prone areas) has 3 regions. The results of this research provide spatial data-based information that can be used as a basis in decision-making for disaster mitigation in Central Java.
RESTful API Implementation in Making a Master Data Planogram Using the Flask Framework (Case Study: PT Sumber Alfaria Trijaya, Tbk) Susanti, Era; Mailoa, Evangs
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1687.924 KB) | DOI: 10.25126/jitecs.202053189

Abstract

One of developing  retail company and is one of the biggest retail companies in Indonesia, namely Alfamart which is owned by PT. Sumber Alfaria Trijaya, Tbk. Alfamart must have the best marketing strategy and increase innovation for the satisfaction of customers in order to survive in high business competition. One strategy to improve marketing is the arrangement of product displays in stores known as planograms. Planogram is a concept that is used in planning the arrangement and placement of products according to certain categories based on consumer spending habits that aim to increase sales at retail. This research was conducted to create a web-based planogram master application using the Flask framework with the python programming language. The method used in this study is the RESTful API, which is the implementation of web services that work through HTTP links. This research produces a web-based master data application that can be used by users in entering data needed in making a planogram.Keywords: RESTful API, Python Flask, Planogram
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 Adi Nugroho Adilla, Denise Sheryl Adinda Setya Oktami Adji, Leonard Surya Adji Andreas A. Sukmana Anugramidah Limbong Madika Aprillia Stefvani Tetikay Arbitra Satria Perdana Arief Wijanarko Arif Hasan Nawawi Azzahra, Windy Livia Bernardus Redika Westama Putra Betty E. Kristiani Bintang Samasto, Revo Budhi Kristianto Candrawira Wicaksana, Gede Abdi Christofael Natalio Dondan Cristanto, Mordekhai Danny Manongga Deasy Carolina Dewi, Teresia Ardika Dhinora, Monica Yoshe Dwi Hosanna Bangkalang Epliani Limbong Rara Erwien Christianto Fairiani, Ayuquinn Astuticein Faqih, Muhammad Faiq Adhitya Fernando, Zuriel Fredrik Landjamara Ndjurumana G.A, Ambrosius Ludang Gabriel Mika Angelo Gede Abdi Candrawira Wicaksana Gilang Agung Saputra Hanita Yulia Harnanda, Elifas Gavra Hartono, Michael Antonius Harun B S O. Mosioi Hendry, - Hindriyanto Dwi Purnomo Hutami Jane Antonia .M Irwan Sembiring Jacob Daan Engel Joseph Triwin Subarjo Juan Rondor Jonathan Sumakul Krisetianto, Wijaya Yoga Krismiyati Krisna Adi Saputra Krisna Setiawan Luhukay, Priscila Karen Madika, Fiwi Fishinsky Magdalena Ariance Ineke Pakereng Muhammad Arifin Muhammad Rizky Pribadi Natacia, Fanny Nina Setiyawati Nopan, Nopan Pakan, Raynaldo Panca Rizki Perkasa Perkasa, Panca Rizki Pramastya, Pragnanta Yopie Putri, Tiara Syah Indra Radius Tanone Santoso, Fian Julio Sri Kasmiyati Sugeng Ariyadi, Faisal Nuryawan Susanti, Era Syahreza Triadhana Marsudi Tad Gonsalves Teguh I. Bayu Teresia Ardika Dewi Tesalonika, Ribka Ventje Jeremias Lewi Engel Widyasari, Elvira Resti Widyastuti, Fransisca Sonia WIJANARKO, ARIEF Willson Mangoki