Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Berkala Forum Studi Transportasi antar Perguruan Tinggi
Maulana, Avin
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Education Engineering Library & Information Science Social Sciences Transportation

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
FAKTOR YANG MEMPENGARUHI PEMILIHAN MODA TRANSPORTASI WISATAWAN DI PANTAI MATAHARI TERBIT DENGAN METODE CHI-KUADRAT Dewi, Ni Made Nanda Pradnya; Dwipayanti, Kadek Viska Eka Mei; Maulana, Avin; Suyasa, Kadek Dwi Pryandana; Mardikawati, Budi
Berkala Forum Studi Transportasi antar Perguruan Tinggi Vol. 1 No. 3 (2023): Berkala Forum Studi Transportasi antar Perguruan Tinggi
Publisher : Prodi Teknik Sipil, Fakultas Teknik, Universitas Jember dan Forum Studi Transportasi antar Perguruan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/berkalafstpt.v1i3.598

Abstract

Transportation as support for accessibility in reaching tourist attraction areas. This’s necessary to develop the tourism sector to be more lively. To support tourism activities, modes of transportation are needed, both private vehicles, public transportation, and online transportation. This study is to determine the characteristics of domestic tourists in the selection of transportation modes to Matahari Terbit Beach. Data were collected using a questionnaire. Data analysis was carried out using the chi-square test of independence with the help software SPSS. The results of the study, 8 indicators that influence the choice of transportation mode, namely age, work, the origin of domicile, time of visit, reasons for choosing the mode, travel distance, travel costs, and length of the trip. Knowing this, it’s hoped can add insight into tourism transportation services in Bali to further improve performance and recovery steps for the tourism sector in Bali to recover soon. ABSTRAK Transportasi sebagai penunjang aksesibilitas dalam menjangkau daerah objek wisata. Hal ini diperlukan agar dapat mengembangkan sektor pariwisata menjadi lebih hidup. Untuk menunjang kegiatan pariwisata dibutuhkan moda transportasi, baik kendaraan pribadi, angkutan umum maupun angkutan online. Tujuan kajian adalah untuk mengetahui karakteristik wisatawan domestik dalam pemilihan moda transportasi menuju Pantai Matahari Terbit. Data dikumpulkan dengan menggunakan kuesioner. Analisis data dilakukan dengan uji chi-square test of independence bantuan software SPSS. Adapun hasil kajian yaitu terdapat 8 faktor yang mempengaruhi pemilihan moda transportasi yaitu usia, pekerjaan, asal domisili, waktu kunjungan, alasan pemilihan moda, jarak tempuh perjalanan, biaya perjalanan, dan lama perjalanan. Dengan mengetahui faktor pemilihan moda wisatawan domestik diharapkan dapat menambah wawasan bagi penyedia jasa transportasi wisata di Bali untuk lebih meningkatkan kinerja serta langkah pemulihan sektor pariwisata di Bali agar segera pulih kembali.
FACIAL INPAINTING IN UNALIGNED FACE IMAGES USING GENERATIVE ADVERSARIAL NETWORK WITH FEATURE RECONSTRUCTION LOSS Maulana, Avin; Fatichah, Chastine; Suciati, Nanik
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a1004

Abstract

Facial inpainting or face restoration is a process to reconstruct some missing region on face images such that the inpainting results still can be seen as a realistic and original image without any missing region, in such a way that the observer could not realize whether the inpainting result is a generated or original image. Some of previous researches have done inpainting using generative network, such as Generative Adversarial Network. However, some problems may arise when inpainting algorithm have been done on unaligned face. The inpainting result show spatial inconsistency between the reconstructed region and its adjacent pixel, and the algorithm fail to reconstruct some area of face. Therefore, an improvement method in facial inpainting based on deep-learning is proposed to reduce the effect of the stated problem before, using GAN with additional loss from feature reconstruction and two discriminators. Feature reconstruction loss is a loss obtained by using pretrained network VGG-Net, Evaluation of the result shows that additional loss from feature reconstruction loss and two type of discriminators may help to increase visual quality of inpainting result, with higher PSNR and SSIM than previous result.
An Enhanced Particle Swarm Optimization with Mutation for Mean-Value-at-Risk Portfolio Optimization in the Indonesian Banking Sector Anam, Syaiful; Bukhori, Hilmi Aziz; Maulana, Avin; Maulana, M. Idam; Rasikhun, Hady
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5191

Abstract

Portfolio optimization in emerging markets is challenging because high volatility and non-normal return distributions reduce the effectiveness of traditional mean–variance models, which tend to underestimate downside risk. This study aims to develop and evaluate an Enhanced Particle Swarm Optimization with Mutation (PSO with Mutation) for portfolio optimization under the Mean-Value-at-Risk (Mean-VaR) framework in the Indonesian banking sector. The novelty of this approach lies in integrating a mutation operator into standard PSO to maintain population diversity, prevent premature convergence, and improve exploration of the solution space. To evaluate the method, daily adjusted closing prices of 31 Indonesian bank stocks from January 2020 to July 2025 were collected. Preprocessing included removing tickers with incomplete data and computing daily returns. The optimization problem was formulated using Mean-VaR as the risk measure, with portfolio weight constraints. The proposed PSO with Mutation was benchmarked against standard PSO, Genetic Algorithm (GA), Bat Algorithm (BA), BA with Mutation, and classical models (Markowitz and Monte Carlo–based VaR). Performance was assessed using expected return, Mean-VaR, risk-adjusted return, Sharpe ratio, execution time, and stability across 25 independent runs. The results show that PSO with Mutation achieved a competitive expected return (0.0020), the lowest Mean-VaR (0.0311), the highest risk-adjusted return (0.0650), and the lowest variability across runs, while maintaining acceptable execution time. These findings confirm that mutation-enhanced PSO provides a robust, balanced, and efficient solution for portfolio optimization, making it highly relevant for investors in volatile emerging markets and advancing research on hybrid metaheuristics in financial optimization.
Improving Lateral-Movement Intrusion Detection in Virtualized Networks using SHAP Feature Selection, SMOTE, and a Voting Ensemble Classifier Maulana, Avin; Anam, Syaiful; Aziz Bukhori, Hilmi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5233

Abstract

Modern virtualized networks, such as those using VXLAN (Virtual eXtensible LAN), generate heavy east–west traffic, which can conceal the lateral movement of attackers. Detecting such infiltration attacks is challenging due to overlay encapsulation (e.g., VXLAN) and flat subnet architectures create blind spots for traditional IDS.  This study aims to evaluate a robust methodology for addressing class imbalance in intrusion detection by integrating SHAP-driven feature selection with SMOTE in a voting ensemble. We conducted an ablation study on the CICIDS2017 Thursday-WorkingHours-Afternoon-Infiltration subset, which is highly imbalanced (36 infiltration flows vs. 288,566 benign flows), varying SHAP feature sets (Top-5 vs. Top-30), classification thresholds , and SMOTE (Synthetic Minority Over-sampling Technique) balancing. The ensemble combined XGBoost, Random Forest, and Logistic Regression, and was evaluated with ROC-AUC, precision, recall, and F1-score. Results indicate that using more SHAP‑important features improves ROC‑AUC and recall, while SMOTE substantially enhances minority‑class detection. The best configuration is Top‑30 SHAP features with SMOTE at , achieved ROC‑AUC = 0.976 and F1‑score = 0.78, whereas using fewer features or omitting SMOTE significantly reduced recall and F1‑score. This synergy of interpretable feature selection and synthetic oversampling establishes a practical methodology for intrusion detection in highly imbalanced, modern virtualized environments. The novelty lies in demonstrating that SHAP + SMOTE integration yields both transparency and resilience, directly addressing encapsulation challenges in detecting stealthy lateral movement.
Co-Authors Aziz Bukhori, Hilmi Bukhori, Hilmi Aziz Chastine Fatichah Dewi, Ni Made Nanda Pradnya Dwipayanti, Kadek Viska Eka Mei Hady Rasikhun Mardikawati, Budi Maulana, M. Idam Nanik Suciati Suyasa, Kadek Dwi Pryandana Syaiful Anam