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All Journal Jurnal Teknologi dan Manajemen Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sains Dan Teknologi (SAINTEKBU) INOVTEK Polbeng - Seri Informatika SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Infotek : Jurnal Informatika dan Teknologi Jurnal Restikom : Riset Teknik Informatika dan Komputer Journal La Multiapp Curtina Jurnal Pengabdian Pada Masyarakat METHABDI Prosiding Seminar Riset Mahasiswa SAINTEK Abdisembrani: Jurnal Pengabdian Masyarakat Jurnal ilmiah teknologi informasi Asia Academia Open
Andik Adi Suryanto
Universitas PGRI Ronggolawe
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Analisis optimasi multi-objektif prestasi mahasiswa dengan algoritma NSGA-II Rochman, Apriatur; Suryanto, Andik Adi; Suprapto, Suprapto
Jurnal Ilmiah Teknologi Informasi Asia Vol 19 No 2 (2025): Volume 19 nomor 2 2025 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v19i2.1201

Abstract

This study investigates the application of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing multiple conflicting objectives related to student academic performance. Using the Student Performance dataset from the UCI Machine Learning Repository, which contains demographic, behavioral, and academic information of 395 secondary school students, the research aimed to maximize final grades (G3), minimize absenteeism, and maximize study time. The study began with exploratory data analysis, which revealed wide variability in academic outcomes, low average absenteeism, and moderate study time, justifying the selection of these three objectives. NSGA-II was then implemented with a population of 100 individuals across 200 generations, employing crossover and mutation operators to generate Pareto-optimal solutions. The results demonstrated diverse non-dominated solutions, illustrating trade-offs between academic achievement, attendance, and study time. Absenteeism emerged as the most significant negative factor, while study time and school support were positively associated with better outcomes. Unlike conventional regression or classification methods that produce a single prediction, NSGA-II provided a spectrum of optimal alternatives, offering flexibility in policy and decision-making. These findings highlight the relevance of multi-objective optimization in education and emphasize the importance of integrating behavioral, social, and digital dimensions to design adaptive strategies for improving student performance.
Food and Beverage Product Review Sentiment Analysis on E-Commerce with Word Embedding and LSTM Bowo, Herry; Suryanto, Andik Adi; Arifia, Amaludin
Journal La Multiapp Vol. 6 No. 5 (2025): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v6i5.2468

Abstract

Sentiment analysis is a widely used method to understand customer opinions about a product. This study aims to analyze the sentiment of food and beverage product reviews on the Tokopedia marketplace using the Long Short-Term Memory (LSTM) approach and word embedding. The data used consisted of customer reviews that were categorized into three sentiment classes, namely positive, neutral, and negative. The model was developed through a series of stages of preprocessing, embedding, training with LSTM, as well as performance evaluation using accuracy and F1-score metrics. The results show that the developed model is able to classify sentiment with a fairly high level of accuracy. Based on the results of the final test on 5,000 data, the model managed to classify 122 data as negative, 130 data as neutral, and 4,871 data as positive, although it still showed an imbalance in class classification. Further analysis through word cloud visualization showed that words like "delicious", "steady", and "good" dominated the positive sentiment, while words like "disappointed", "broken", and "slow" often appeared in negative sentiment. This study provides valuable insights for businesses in understanding customer opinions and improving the quality of products and services.
Rancang Bangun Sistem Parkir Menggunakan Optical Character Recognition (OCR) Untuk Mendeteksi Plat Nomor Kendaraan Berbasis Arduino Dito, Himawan Pramu; Amaluddin, Fitroh; Suryanto, Andik Adi; Rachmawati, Siti
Prosiding Seminar Riset Mahasiswa Vol 1, No 1: Maret 2023
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Parkir merupakan keadaan suatu kendaraan yang tidak bergerak yang bersifat sementara dengan pengemudi meninggalkan kendaraannya. Sistem parkir yang tertata dengan baik akan membuat pengguna kendaraan merasa nyaman serta pengelolaan parkir menjadi lebih efektif dan efisien. Salah satu pemanfaatan teknologi yang dapat memecahkan permasalahan tersebut adalah dengan membuat sistem manajemen parkir menggunakan pengolahan citra digital dengan melakukan pengenalan pola yang memanfaatkan metode image processing Optical Character Recognition (OCR) pada gambar plat nomor kendaraan yang telah diambil ketika kendaraan masuk ke area parkir. Berdasarkan percobaan yang telah dilakukan, simulasi ini menunjukkan bahwa sistem dalam peneliti ini dpat bekerja dengan baik antara sensor alat yang digunakan, printer thermal yang dapat mencetak lokasi kosong tempat parkir serta website yang dapat menampilkan riwayat data kendaraan yang telah menggunakan tempat parkir.Keyword: parkir, arduino, image processing, Optical Character Recognition
Hybrid Transformer-LSTM for Stock Price Prediction with Monte Carlo Testing of Loss Levels Saputra, David Andris Rizky; Muqtadir, Asfan; Suryanto, Andik Adi
Academia Open Vol. 11 No. 1 (2026): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.11.2026.13521

Abstract

General Background: Stock price prediction is a complex problem due to the non-linear, stochastic, and volatile characteristics of financial markets. Specific Background: Advanced deep learning approaches such as Long Short-Term Memory (LSTM) and Transformer architectures have been applied to capture sequential patterns and global dependencies in time-series financial data. Knowledge Gap: However, existing approaches often lack integration between accurate forecasting and quantitative risk measurement within a unified framework. Aims: This study proposes a Hybrid Transformer–LSTM model integrated with Monte Carlo simulation to provide both precise stock price prediction and risk evaluation. Results: Using historical daily stock price data of BMRI from March 2013 to March 2025 and incorporating technical indicators such as RSI and moving averages, the model achieved a Mean Absolute Percentage Error of 4.13% and a Mean Absolute Error of 246.35 Rupiah. Monte Carlo-based Value at Risk at a 99% confidence level estimated a potential maximum loss of 5.35%. Novelty: The study combines sequential learning, attention mechanisms, and probabilistic simulation in a single framework linking prediction accuracy with risk quantification. Implications: The proposed approach provides a comprehensive analytical basis for supporting investment decision-making through reliable forecasting and measurable downside risk estimation. Highlights : Combined deep learning architecture produces low forecasting error on long-term historical data Probabilistic simulation quantifies maximum potential loss under high confidence level Integrated framework links predictive modeling with measurable investment risk Keywords: Hybrid Transformer LSTM, Stock Price Prediction, Monte Carlo Value at Risk
Application of VGG16 Deep Learning Architecture and K-Means for PCOS Severity Clustering Based on Ovarian Ultrasound Images Nurul Hidayah; Asfan Muqtadir; Andik Adi Suryanto
Jurnal Teknologi dan Manajemen Informatika Vol. 12 No. 1 (2026): Juni 2026
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v12i1.16922

Abstract

Polycystic Ovary Syndrome (PCOS) is a complex hormonal disorder that significantly impacts the reproductive and metabolic health of women. A primary challenge in PCOS analysis is the reliance on subjective manual observations of ultrasound (USG) images, which are prone to inconsistency due to the lack of objective standards. This study proposes an automated, unsupervised framework to stratify the morphological characteristics of PCOS ultrasound images without predefined clinical labels. The method integrates feature extraction using the VGG16 deep learning architecture with K-Means clustering. The structural integrity of the computational clusters was quantitatively evaluated, achieving a Silhouette Score of 0.4248 and a Davies-Bouldin Index (DBI) of 0.9175, indicating the successful formation of distinct morphological groupings. Furthermore, an evaluation using a Support Vector Machine (SVM) to assess the internal consistency and linear separability of the extracted features yielded a test accuracy of 97.54%. The results demonstrate that the integration of VGG16 and K-Means effectively partitions PCOS ultrasound images into objective, computationally stable morphological groups. This approach is designed to function as a standardized clinical decision support tool, mitigating visual subjectivity and providing a measurable baseline for women's reproductive health management.
Sosialisasi literasi digital cakap dan bijak menggunakan dompet elektronik di SMK Sunan Bejagung Tuban Andik Adi Suryanto; Amaludin Arifia; Alfian Nurlifa; Asfan Muqtadir; Fitroh Amaluddin; Aris Wijayanti; Andy Haryoko; Suprapto Suprapto; Miftahul Ulum; Nurul Hidayah; Layla Ayu Mustika Putri; Dhoni Prasetya; Syahrul Andrian Setia Budi; Rachmalia Vinda Kusuma; Miftahul Munir; Saeful Mizan; Yosia Dian Purnama Windrayadi; Mardi Widodo; Tabhita Sri Hartati Wulandari; Guruh Putro Dirgantoro
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 10, No 3 (2026): June
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v10i3.38805

Abstract

Abstrak Perkembangan transaksi keuangan digital di kalangan pelajar menuntut peningkatan literasi keamanan finansial digital. Kegiatan pengabdian masyarakat ini bertujuan meningkatkan pemahaman siswa kelas XII SMK Sunan Bejagung Tuban terkait penggunaan dompet elektronik secara aman, pencegahan kejahatan siber, serta verifikasi legalitas layanan keuangan digital. Metode yang digunakan berupa sosialisasi, simulasi praktik keamanan akun, diskusi interaktif, serta evaluasi melalui pretest, posttest, dan angket respon. Kegiatan melibatkan 68 siswa dan menunjukkan peningkatan pemahaman signifikan dari kategori kurang menjadi sangat baik setelah kegiatan. Respon peserta juga sangat positif sehingga kegiatan dinilai efektif meningkatkan literasi keamanan finansial digital. Secara kuantitatif, hasil analisis deskriptif menunjukkan peningkatan kategori pemahaman sangat baik dari 1,7% (pretest) menjadi 82,8% (posttest), serta penurunan kategori kurang dari 66,8% menjadi 0%, yang dihitung berdasarkan persentase jumlah responden pada tiap skala penilaian instrumen pretest dan posttest (N=68). Kata kunci: literasi digital; dompet elektronik; keamanan siber; pelajar SMK; pengabdian masyarakat. AbstractThe rapid growth of digital financial transactions among students has increased the need for stronger digital financial security literacy. This community service program aimed to enhance the understanding of Grade 12 students at SMK Sunan Bejagung Tuban regarding the safe use of electronic wallets (e-wallets), cybercrime prevention, and verification of the legitimacy of digital financial services. The program employed educational sessions, hands-on simulations of account security practices, interactive discussions, and evaluations using pretests, posttests, and participant response questionnaires. A total of 68 students participated in the program. The findings demonstrated a substantial improvement in participants’ understanding, shifting from a poor level before the program to a very good level after its completion. Participants also provided highly positive responses, indicating that the program was effective in strengthening digital financial security literacy. Quantitatively, descriptive analysis showed that the proportion of participants in the very good understanding category increased from 1.7% in the pretest to 82.8% in the posttest, while the proportion in the poor category decreased from 66.8% to 0%. These percentages were calculated based on the number of respondents within each rating category of the pretest and posttest instruments (N = 68). Keywords: digital literacy; electronic wallets; cybersecurity; vocational high school students; community service.
Pengaruh Inkonsistensi Switching Light-Dark Mode Antar Aplikasi Terhadap Mental Workload dan Kinerja Pengguna Smartphone Layla Ayu Mustika Putri; Alfian Nurlifa; Andik Adi Suryanto
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9579

Abstract

Visual inconsistency in user interface contexts, such as visual theme changes during mode switching (from Light to Dark Mode), is often considered an optional aesthetic element; however, its impact on user cognitive load and performance has not been widely researched. This study aims to analyze the effect of mode switching on subjective cognitive load, task completion time efficiency, and user performance accuracy in an interface interaction context. The method used in this experiment was a Quasi-Experimental Design with a posttest-only control group type, involving 40 respondents divided into two groups: static theme condition and mode switching condition. Subjective cognitive load was measured using the NASA-TLX instrument, while objective performance was evaluated through Task Completion Time and Error Rate. The results of this study indicate that the treatment group experienced an increase in cognitive load, with an average WWL score of 55.23, compared to the control group at 42.50. The frustration dimension was the highest, at 28.0, indicating emotional pressure due to interface inconsistency. Objectively, the application of mode switching slowed down task completion time by 3.65 seconds and increased the error rate by up to two times. Statistical test results also showed differences in all research variables, with a p-value < 0.005 and an effect size value on cognitive load of 0.816 (Large Effect). These findings lead to the conclusion that visual stability is an important factor in interface design, necessary for maintaining navigation efficiency, increasing user comfort, and minimizing interaction errors.
Co-Authors Abdullah Nur Huda Adityo Nugroho, Adityo Akmalul Mu’minin Al Mubarok, Bagus Alam, Sitti Nur Alfa Nurfahma Rosalita Alfia Nurlifa Alfian Nurlifa Alfian Nurlifa Alfian Nurlifa Alfianisa Hanny Saputri Amaluddin Arifia Amaludin Arifia Amaludin Arifia Amin Masnun Ammar Ma'ruf Andri Tri Setiawan Andy Haryoko Arifia, Amaludin Arina Rosyida Aris Wijayanti Aris Wijayanti Aris Wijayanti Asfan Muqtadir Asfan Muqtadir Asfan Muqtadir Asfan Muqtadir Azifatin Ni&#039;ayah Bangkit Setyawan, Dany Meiko Bowo, Herry Dewi, Lestari Rozita Dhoni Prasetya Dito, Himawan Pramu Diva Elydiya Yahya Dodik Jihar Ananta Dwi Kurnia Basuki Dwi Yulianto Eko Prayudi Yustisio Farkhan, Muhamad Farir Fitroh Amaluddin Fitroh Amaluddin Fitroh Amaluddin Fitroh Amaludin Ghozali, Daniel Reredo Ahmad Guruh Putro Dirgantoro Heru Prastyo Ihda Maulidia Nurul Farida Imron Rosyidi, Imron Iwan Adhicandra Karya Suhada Kholid Fathoni Kraugusteeliana Kraugusteeliana Krishna Tri Sanjaya Layla Ayu Mustika Putri Layla Ayu Mustika Putri Luluk Purwanti M.Farid Musthofa Mahendra Dodik Sugiyanto Mardi Widodo Marita Ika Joesidawati Mar’atus Sholihah Mellynda Oktaviana Miftahul Munir Miftahul Ulum Miftahurrohman Nafisah, Jauharotun Nia Maulina Ridiani Nur Suci Rahayu Nurul Hidayah Nurul Hidayah Prakoso, Adityo Dwi Puspitaningrum, Fitria Putra, Muharrom Yoga Putri Milenia Rachmalia Vinda Kusuma Rifta Dewi Fortuna Rika Harnita Ririn Safitri Rochman, Apriatur Saeful Mizan, Saeful Sahla Saqilla Saputra, David Andris Rizky Saputri, Alfianisa Sari, Fitria Atika Sarofah, Maratus Sasmita, Niken Diah Siti Nurjanah SITI RACHMAWATI Siti Rachmawati Siti Rachmawati Sitti Nur Alam Suprapto SUPRAPTO Suprapto Suprapto Suwarsih Suwarsih Syahrul Andrian Setia Budi Tabhita Sri Hartati Wulandari Thoyyib Mau lana Muhtadin Tsalis Rahmawati Uswatun Chasanah Wahyu Candra, Moh. Wenda, Alex Widjaja, Warkianto Wijaya, Hamid Yosia Dian Purnama Windrayadi Yuliana, Nian Yulistyadi Firman Dwi P. Zaenal Fanani