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All Journal Jurnal Media Infotama Techno.Com: Jurnal Teknologi Informasi JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Informatika dan Teknik Elektro Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization Indonesian Journal of Artificial Intelligence and Data Mining JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat To Maega: Jurnal Pengabdian Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi bit-Tech Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jurnal Abdi Insani Journal of Advanced in Information and Industrial Technology (JAIIT) Nusantara Science and Technology Proceedings Kalam Cendekia: Jurnal Ilmiah Kependidikan Journal of Information System and Technology (JOINT) TIERS Information Technology Journal International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Iqtisaduna COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Informatika Dan Tekonologi Komputer (JITEK) Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE Jurnal Informatika Teknologi dan Sains (Jinteks) Concept: Journal of Social Humanities and Education JIKTI : Jurnal Ilmiah Komputer Terapan dan Informasi Mujtama': Jurnal Pengabdian Masyarakat Journal of Artificial Intelligence and Digital Business TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi ILTEK : Jurnal Teknologi Jurnal Elektronika dan Teknik Informatika Terapan Horizon: Indonesian Journal of Multidisciplinary Modem : Jurnal Informatika dan Sains Teknologi Joong-Ki Jurnal Informatika Dan Tekonologi Komputer Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis Mujtama’ Jurnal Pengabdian Masyarakat Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Muhammad Muharrom Al Haromainy
Universitas Pembangunan Nasional Veteran Jawa Timur
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IMPLEMENTASI METODE K-MEANS DAN HARMONY SEARCH UNTUK MENYEDERHANAKAN DATA PADA CREDIT SCORE Anita Puspitasari; Eka Prakarsa Mandyartha; Muhammad Muharrom Al Haromainy
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.6952

Abstract

Credit Score merupakan sebuah penilaian yang digunakan untuk memperkirakan risiko kredit. Penelitian ini dilakukan dengan tujuan untuk menyederhanakan data serta meningkatkan akurasi prediksi pada Credit dengan menggunakan metode K-Means dan Harmony Search. K-Means digunakan untuk mengelompokkan fitur berdasarkan korelasi jarak (Distance -based) guna untuk mengurangi redundansi dan menemukan fitur paling representatif. Selanjutnya, Harmony Search digunakan untuk mencari kombinasi fitur terbaik terhadap target prediksi dengan menyesuaikan kombinasi parameter HMS, HMCR, dan PAR. Untuk evaluasi kinerja model dilakukan menggunakan Linear Regression dengan nilai metrik MAPE, . Hasil penelitian menunjukkan bahwa kombinasi parameter terbaik yaitu HMS = 30, HMCR = 0.7, PAR = 0.1. Hal ini dilihat dari evaluasi kinerja model yang menghasilkan 10 fitur dengan metrik MAPE sebesar 3.4%, dan sebesar 0.84. Metode ini terbukti mampu meningkatkan akurasi dan efisiensi komputasi dibandingkan model tanpa seleksi fitur. Dengan demikian, kombinasi metode K-Means dan Harmony Search terbukti efektif dalam menyederhanakan data serta meningkatkan kinerja model pada Credit Score.
Cloud-Based High Availability Architecture Using Least Connection Load Balancer and Integrated Alert System Prinafsika; Achmad Junaidi; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2520

Abstract

Ensuring optimal service continuity remains a critical challenge in cloud computing, especially when dealing with high traffic loads and system failure potential that can cause losses. To address this, this research presents the implementation of a high availability (HA) cloud system using the Least Connection load balancing algorithm implemented with Nginx, integrated with early anomaly detection and alert mechanisms. The HA architecture is implemented across two geographically distributed cloud service providers, Alibaba Cloud and Google Cloud, to analyze latency and performance differences under high load conditions. The system's resilience and scalability were evaluated through load testing using K6, simulating workloads ranging from 100 to 1000 Virtual Users (VUs) for single server configurations and 200 to 2000 VUs for HA configurations. The experiment results showed a significant improvement in service availability, reaching 100% uptime with the HA configuration compared to a peak of 98.79% in the single server environment. The Least Connection strategy effectively balanced traffic by monitoring active connections, resulting in a 29.73% increase in processed requests and a 42% reduction in system load at 1000 VUs. Additionally, the alert system successfully sent real-time Telegram notifications for delays or failures, enabling proactive mitigation. These results confirm that combining dynamic load balancing with proactive alerts can significantly improve service reliability, resource efficiency, and resilience to failures in distributed cloud infrastructure providing a viable model for robust and scalable cloud service architectures.
Prediction of Air Pollution Standard Index Using CEEMDAN-LSTM Rafie Ishaq Maulana; Muhammad Muharrom Al Haromainy; Achmad Junaidi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3164

Abstract

Air pollution has become a critical environmental issue, particularly in urban areas such as DKI Jakarta, where pollutant concentrations frequently reach the highest levels in Indonesia. Accurate prediction of the Air Pollution Standard Index (ISPU) is essential for mitigating the adverse health and environmental impacts of poor air quality. However, ISPU data exhibit nonlinear, volatile, and non-stationary characteristics, posing challenges for conventional prediction models. To overcome these challenges, this study proposes a hybrid Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Long Short-Term Memory (CEEMDAN–LSTM) model, applied to daily ISPU data from 2010 to 2025 comprising 5,686 records. CEEMDAN was selected over conventional decomposition methods such as EEMD and VMD due to its ability to suppress mode-mixing and extract more stable Intrinsic Mode Functions (IMFs) through adaptive noise addition, thereby enhancing signal interpretability and learning efficiency. The ISPU time series was decomposed into multiple IMFs, and the resulting components were reconstructed and modeled using an optimized LSTM architecture obtained through Bayesian hyperparameter tuning. The optimal configuration batch size of 54, dropout rate of 0.37, and hidden units of 6, 33, and 34 achieved an RMSE of 14.0, reflecting a substantial improvement over the baseline LSTM model. The results demonstrate that integrating CEEMDAN with LSTM effectively reduces signal complexity, stabilizes convergence, and improves forecasting accuracy for non-stationary air quality data in DKI Jakarta. This modeling framework provides a robust foundation for developing predictive early-warning systems, supporting evidence-based environmental policy, and enhancing public health preparedness in rapidly urbanizing regions.
Classification Tuberculosis on Chest X-Ray Images Using Backpropagation Neural Network Ananda Ayu Puspitaningrum; Anggraini Puspita Sari; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3197

Abstract

Tuberculosis is an infectious disease that primarily affects the lungs and remains a major health concern due to the difficulty of diagnosis through manual interpretation of chest X-ray images. This study aims to develop an automatic tuberculosis classification system using the Backpropagation Neural Network (BPNN) method to improve diagnostic accuracy. The dataset used in this study was obtained from the Kaggle Tuberculosis (TB) Chest X-ray Dataset, consisting of 7.000 images divided into two classes normal and tuberculosis. The research stages include image preprocessing such as conversion to grayscale, resizing to 256×256 pixels, contrast enhancement using histogram equalization, and noise reduction using a median filter. Experiments were conducted by varying the number of hidden layers 2, 3, and 4 to analyze the effect of network architecture complexity on classification performance. The results showed that the configuration with 2 hidden layers and [100 50] neurons achieved the best performance with an accuracy of 93.57%. The findings indicate that deeper network architectures do not always guarantee higher accuracy and may increase computational load. Overall, this configuration provides an optimal balance between learning capability and accuracy, demonstrating the potential of the BPNN method in supporting early and reliable tuberculosis detection through machine learning based chest X-ray image analysis for clinical decision support.
Classification of Jombang Batik Motifs Using Ensemble Convolutional Neural Network Riza Satria Putra; Muhammad Muharrom Al Haromainy; Achmad Junaidi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3204

Abstract

Batik, recognized by UNESCO as an Intangible Cultural Heritage, presents complex visual patterns that challenge automated classification systems. The intricate variations in texture, color, and geometry across motifs often lead to inconsistent performance in single Convolutional Neural Network (CNN) models, which struggle to generalize across subtle inter-class differences. To address these limitations, this study implements an Ensemble CNN framework to classify six Ploso Jombang batik motifs Garudan, Merak Kinasih Keyna Galeri, Ploso Bersemi, Jombang Berseri, Sulur Kangkung, and Burung Hong from a dataset of 2,134 images. The proposed approach integrates three pre-trained architectures EfficientNetB0, ResNet18, and VGG16 through a stacking ensemble strategy to leverage complementary feature extraction capabilities. Experimental results demonstrate that EfficientNetB0 achieved the highest individual accuracy (94%), while VGG16 recorded the lowest (60%). When combined, the ensemble configurations EfficientNetB0 + VGG16 and EfficientNetB0 + ResNet18 achieved peak test accuracies of approximately 96.88% on 321 test samples, reflecting a 2.88% improvement over the best single model. Confusion Matrix analysis confirmed robust model stability, with 100% accuracy for motifs such as Ploso Bersemi and Sulur Kangkung. These results validate that ensemble learning effectively mitigates overfitting and enhances generalization by aggregating diverse visual representations. The proposed model thus provides a reliable computational framework for automated batik classification and digital cultural preservation, supporting Indonesia’s efforts to document, catalog, and sustain its traditional heritage through artificial intelligence–driven methods.
Implementation of the Weighted Product Method for Determining Poor Households Alya Izzah Zalfa Rihadah Ramadhani Nirwana Putri; Afina Lina Nurlaili; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3214

Abstract

Many village-level poverty programs still depend on manual deliberation, which is slow to audit and difficult to reproduce across localities. This study addresses that gap by delivering an end-to-end, transparent implementation of the Weighted Product (WP) method for ranking poor households in Prunggahan Kulon, Tuban Regency. We assess whether a clearly specified WP pipeline complete with documented polarity (benefit/cost), normalized weights, and run logs can convert heterogeneous village records into reproducible preferences suitable for operational targeting. Household data supplied by the village and the Social Office were coded on a 0–1 scale for eight agreed criteria; expenditure (C2) was treated as a cost while others were benefits. Equal weights were used in this initial deployment for clarity and explainability. The method was implemented in a Laravel-based system that records bases, signed exponents, the multiplicative score , and normalized preferences . A five-household subset (A1–A5) is reported for illustration, with the full system supporting larger lists. The computation yielded a clear ordering (A4 > A1 > A2 > A3 > A5). The multiplicative rule preserved penalties for critical shortfalls and prevented strong indicators from masking severe deprivations, while the software artifacts ensured traceability from inputs to final . The dataset comprised 491 households encoded across eight criteria, with one cost criterion and seven benefits. Compared with prior WP applications, our contribution is an end-to-end, district-ready pipeline with explicit polarity, documented weights, and preserved run logs enabling third-party replication. This design measurably improves transparency and reproducibility for local poverty targeting.
Performance Evaluation of YOLOv5su and SVM With HOG Features for Student Attendance Face Recognition Achmad Rozy Priambodo; Achmad Junaidi; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3215

Abstract

The rapid evolution of Artificial Intelligence (AI) and Computer Vision has revolutionized conventional attendance systems by introducing automated and intelligent alternatives. Traditional approaches such as manual entry and fingerprint-based systems are often inefficient, error-prone, and unsuitable for large-scale student management. This study evaluates a hybrid face recognition framework that combines You Only Look Once version 5 su, Histogram of Oriented Gradients (HOG), and Support Vector Machine (SVM) to automate student attendance. The YOLOv5su algorithm performs fast and lightweight face detection, while HOG extracts gradient-based facial descriptors classified by SVM. Experiments were conducted using a facial image dataset consisting of 500 original images from 10 classes (50 images per class), which were augmented to 3,500 images with variations in pose, expression, and illumination. The proposed YOLOv5sU–HOG–SVM model achieved 97.1% detection accuracy and 97% recognition accuracy, with mean precision, recall, and F1-score values of 0.98, outperforming conventional CNN-based hybrid models in both accuracy and computational efficiency. These results demonstrate that the combination of YOLOv5su, HOG, and SVM provides a novel balance between detection speed and recognition robustness, making it suitable for real-time academic attendance management. Future work should integrate transformer-based facial feature extraction to further enhance robustness under extreme conditions and larger-scale datasets.
Comparative Analysis of IndoBERT, IndoBERTweet, and XLM-RoBERTa for Detecting Online Gambling Comments on YouTube Kevin Iansyah; Afina Lina Nurlaili; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3257

Abstract

The proliferation of online gambling promotions in YouTube comment sections poses significant challenges for content moderation on Indonesian digital platforms. Although transformer models have proven effective for various Indonesian-language NLP tasks, no systematic comparative evaluation exists for detecting online gambling promotions on YouTube, nor has research explored model sensitivity to hyperparameters in this context. This research identifies the optimal transformer model and configuration for detecting Indonesian-language online gambling promotion comments on YouTube. A total of 26,455 YouTube comments were collected from February to July 2025 and stratified into balanced training (18,926 comments) and validation sets (3,340 comments), plus an imbalanced testing set (4,189 comments with 28.05% promotions and 71.95% non-promotions) reflecting realistic platform conditions. Nine fine-tuning experiments were conducted with three transformer models (IndoBERT, IndoBERTweet, XLM-RoBERTa) using three learning rates (1e-5, 2e-5, 3e-5). Evaluation employed accuracy, precision, recall, F1-score, and AUC-ROC metrics. Results show IndoBERT with learning rate 1e-5 achieved best performance (F1-score 99.57%, recall 99.49%), outperforming IndoBERTweet (F1-score 98.58%) and XLM-RoBERTa (F1-score 99.28%). Interestingly, the formal corpus-trained model (IndoBERT) proved more effective than the social media model (IndoBERTweet), indicating that gambling promotion language patterns tend to be structured despite appearing in informal contexts. IndoBERT demonstrated greatest stability to learning rate variations (standard deviation 0.0011), while XLM-RoBERTa offered fastest inference time (2.48 ms) with optimal performance-efficiency balance. These findings provide practical recommendations for automated content moderation systems on Indonesian social media platforms, with IndoBERT for maximum accuracy scenarios and XLM-RoBERTa for large-scale real-time deployment.
Comparative Analysis of Memory and Render Performance: BLoC vs Provider on Low-End Devices Muhammad Albert Nur Agathon; Afina Lina Nurlaili; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3268

Abstract

Mobile application performance is a critical determinant of user retention, yet optimization strategies for low-end hardware remain underexplored in the Flutter ecosystem. The choice of state management, specifically between Provider and BLoC, is a pivotal architectural decision affecting resource efficiency, particularly when integrated with Clean Architecture. Addressing the scarcity of empirical studies on constrained hardware, this research quantitatively compares the performance of these two libraries on a Vivo 1719 device running Android 7. Two identical Al-Qur'an applications were developed to facilitate a controlled experiment, isolating state management as the single variable. Performance metrics, including Resident Set Size (RSS), Garbage Collection (GC) frequency, and Frame Janks, were measured using Flutter DevTools during intensive scrolling and data loading scenarios. The results demonstrate that BLoC significantly outperforms Provider on low-end specifications. In the heaviest scenario, BLoC recorded lower peak memory usage (201.88 MB) compared to Provider (221.64 MB) and triggered 33% fewer GC events. Furthermore, BLoC reduced frame janks by 40% (15 janks vs. 21 janks). From a software engineering perspective, these findings indicate that BLoC's stream-based, event-driven architecture offers superior resource isolation compared to Provider's listener propagation mechanism, which tends to induce higher garbage collection overhead. Consequently, this study recommends BLoC as the preferred strategy for deployments targeting emerging markets, offering a worthwhile trade-off between development complexity and runtime stability to ensure broader digital inclusivity.
Design and Development of a Real-Time Delivery Tracking App Using WebSocket and Haversine Bima Arya Kurniawan; Retno Mumpuni; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3275

Abstract

The increasing demand for reliable real-time delivery tracking in furniture logistics highlights critical gaps in existing solutions: static geofencing without precise geodesic validation, polling-based protocols causing 1-3 second delays, and non-integrated Proof of Delivery (PoD) lacking automated location verification. These limitations result in inconsistent validation accuracy and limited transparency, directly impacting customer trust and operational costs. This research addresses these gaps by developing a novel tracking system uniquely integrating WebSocket-based bidirectional communication, Location-Based Services (LBS), and the Haversine Formula for geodesic distance validation, a combination not previously implemented for PoD-specific furniture logistics. The system comprises an Android driver application, Laravel-based backend with real-time WebSocket infrastructure, and monitoring interfaces for administrators and customers. Evaluation through 30 latency samples demonstrated stable WebSocket performance with average latency of 44.03 ms (SD=4.38ms; 95% CI=44.03±1.62ms), achieving 23-68 times faster transmission than traditional HTTP polling. Three geofence validation scenarios (50m, 100m, 200m radii) achieved 100% accuracy, with PoD triggering consistently restricted to authorized boundaries. Comprehensive functional testing confirmed reliable operational behavior across mobile and web modules. The practical impact includes automated location verification eliminating manual validation time, enhanced fleet visibility for large delivery trucks, and increased customer transparency through continuous tracking. These findings demonstrate that integrated WebSocket-Haversine-LBS architecture significantly improves delivery transparency, reduces validation errors, and enhances operational decision-making in furniture logistics. Future priorities include offline-first architecture, route optimization for heavy vehicles, and iOS platform support.
Co-Authors Abdillah, Ikhwan Abdul Rezha Efrat Najaf Achmad Andrian Maulana Achmad Junaidi Achmad Rozy Priambodo Agung Mustika Rizki, Agung Mustika Agus Wibowo Agus Zainal Arifin Ahmad Saikhu Akbar, Fawwaz Ali Al Danny Rian Wibisono Al Fatih, Abdullah Alya Izzah Zalfa Rihadah Ramadhani Nirwana Putri Ananda Ayu Puspitaningrum Andreas Nugroho Sihananto Angga Lisdiyanto Anggraini Puspita Sari Anggraini Puspita Sari Anita Puspitasari Annisa Dwi Puspitarini Anugerah, Rico Putra Arraya Akhsa Putra Priyadizah ASHARI, FAISAL Avi Sunani Aviolla Terza Damaliana Azira, Volem Alvaro Azira Basuki Rahmat Masdi Siduppa Bima Arya Kurniawan Budi Nugroho Chairil, Augustin Mustika Chastine Fatichah Christianty, Theressa Marry Clara Diva Paramitha Darmawan, Marcellinus Aditya Vitro Dhimas Wahyu Prayogi Dian Maharani Dinda Friska Oktaviana Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Edi Sugiyanto Eva Yulia Puspaningrum Fania Imelda Safitri Faris Syaifulloh Farkhan Fauzi, Zaky Ahmad Ferdi Firdaus Ega Pratama Ferry Trilaksana Putra Fetty Tri Anggraeny Firza Prima Aditiawan Fitrani, Laqma Dica Ganal Arief Rahmawan Gusti Eka Yuliastuti Hajjar, Debrina Octrisya Hardiansyah, In Naka Malik Henni Endah Wahanani Hidra Amnur I Wayan Alston Argodi I Wayan Alston Argodi Istian Kriya Almanfakulti Jeziano Rizkita Boyas Kartini Kartini Kevin Iansyah Kusuma Wardani, Amalia Dwi Lailatul Musyaffaah Lina Nurlaili, Afina Lintang Putri Permatasari Lusi Kurnia Lusian Nandang Arjamulia M. Sa’aduddin Abdillah Yusuf Mandyartha, Eka Prakarsa Maulana Herza, Fakhri Maulana, Hendra Maulana, Vieri Arief Moh. Angga Ardiyansyah Mohammad Habim Hazidan Rifqi Mohammad Setyo Wardono Muhamad Fihris Aldama Muhammad Albert Nur Agathon Muhammad Baihaqi Arrisalah Muhammad Daffa Arifin Muhammad Helmi Satria Fedianto Muhammad Izdihar Alwin Muhammad Rifaldi Syaril Mashafy Muzdalifah, Nayani Alya Aquila Nia Dwi Puspitasari Nugroho, Budi Nur Nafisatul Fitriyah Nurlaili, Afina Lina Nurlaili, Afina Lina Permatasari, Reisa Pratama Wirya Atmaja Prinafsika Putra, Chrystia Aji Putra, Gredy Christian Hendrawan Raden Kokoh Haryo Putro Rafie Ishaq Maulana Rafif Ilafi Wahyu Gunawan Ramadhani, Muhammad Nabil Retno Mumpuni Reza, Reno Alfa Riza Satria Putra Rizka Fadhillah, Irnanda Ryan Purnomo Samodera, Bayu Sari, Rizky Buana Satrio, Deva Dwi Setyawan, Dimas Ari Shalehuddin Albawani, Raden Siregar, Talitha Aurora Nadenggan Sujayanti, Forentina Kerti Pratiwi Suprapti Taufiqqurrahman, Husain Tompo Panjaitan Tri Septianto Trimono, Trimono Triyana, Dimas Volem Alvaro Azira Azira Wahyu Eko Pujianto Wahyu Fahrul Ridho Wahyu Syaifullah JS Waluya, Onny Kartika Waskito, Achmad Derajat Winarti ., Winarti Yisti Vita Via