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All Journal Jurnal Algoritma Jurnal Wawasan Ilmiah Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) Jurnal Ilmu Sosial dan Ilmu Politik JOIN (Jurnal Online Informatika) Edu Komputika Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Teknologi Informasi Komunikasi dan Industri JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal ULTIMATICS Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURTEKSI Jurnal Sistem Cerdas Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Teknika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal PkM MIFTEK Jurnal Algoritma Engineering Science Letter Journal of Novel Engineering Science and Technology IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Integrated : Journal Of Information Technology and Vocational Education JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Journal of Intelligent Systems Technology and Informatics International Journal of Applied Information Systems and Informatics Jurnal Elektronika dan Telekomunikasi
Dede Kurniadi
Institut Teknologi Garut
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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Perancangan Sistem E-Voting Mahasiswa Berbasis Web Menggunakan RUP untuk Efisiensi Pemilihan Ketua Organisasi Kampus Rahayu, Raden Erwin Gunadhi; Kurniadi, Dede; Pratama, Reifalga Gais
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2267

Abstract

The Student Executive Board (BEM) is an intra-campus student organization that serves as the executive body at the university level, led by a BEM president. Currently, the BEM election is conducted conventionally, resulting in a decline in voter participation due to factors such as students being on vacation, having class schedules, or lacking interest in organizational activities. Therefore, a more effective election process is needed—one solution being the implementation of E-Voting, which allows all students to participate digitally. The objective of this study is to develop an e-voting system that facilitates students in electing the BEM president of the Institut Teknologi Garut, making it easier for them to exercise their voting rights. The method used is the Rational Unified Process (RUP), which consists of four main phases: Inception, Elaboration, Construction, and Transition. The black box testing results indicate that all system functions operate according to the defined scenarios. Meanwhile, beta testing involving 33 respondents achieved a satisfaction rate of 88.48%, categorized as strongly agree. These results demonstrate that the developed e-voting system meets users’ functional requirements, simplifies the committee’s work in managing the election process, and provides students with convenient access to candidate information, vision and mission statements, as well as real-time election results. The contribution of this research lies in presenting an e-voting system that is not only efficient, dynamic, and flexible but also developed using the iterative RUP methodology, ensuring software quality and alignment with student organizational needs. Moreover, it serves as a reference for implementing digital voting technology within academic environments.
Implementasi Modul Tanda Tangan Digital dengan Superenkripsi RSA-ECDSA dan SHA-512 Pada Sistem Informasi Akademik Sekolah Ajif, Arvin Muhammad; Nuraeni, Fitri; Kurniadi, Dede; Elsen, Rickard
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2353

Abstract

Keamanan dokumen akademik pada sistem informasi sekolah merupakan aspek krusial untuk menjamin integritas dan keaslian data. Penelitian ini mengusulkan pengembangan modul tanda tangan digital berbasis superenkripsi RSA–ECDSA dengan fungsi hash SHA-512 pada sistem Buku Induk Nilai berbasis web. Metode pengembangan menggunakan Rapid Application Development (RAD) untuk menghasilkan prototipe cepat sesuai kebutuhan pengguna. Proses implementasi meliputi hashing dokumen menggunakan SHA-512, enkripsi hasil hash dengan RSA, penandatanganan menggunakan ECDSA, serta penyisipan tanda tangan digital dalam bentuk QR-Code ke dokumen ekspor. Pengujian dilakukan pada 30 sampel dokumen dengan variasi ukuran 10 KB–1 MB melalui pendekatan blackbox testing. Hasil uji menunjukkan tingkat keberhasilan verifikasi dokumen sebesar 100%, waktu rata-rata proses penandatanganan 1,25 detik, dan waktu verifikasi 0,98 detik. Sistem terbukti mampu mendeteksi perubahan sekecil 1 byte, menjaga integritas dokumen, dan memberikan tingkat keamanan setara 112–128 bit sesuai standar kriptografi modern. Temuan ini menegaskan efektivitas superenkripsi RSA–ECDSA dalam meningkatkan keamanan dokumen akademik serta menawarkan solusi yang efisien, portabel, dan dapat diverifikasi secara mandiri melalui QR-Code.
Text Mining-Based Sentiment Analysis of ChatGPT Users on X Platform Using Naïve Bayes Algorithm Alkamal, Chaerulsyah; Kurniadi, Dede
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 2 (2025): November 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i2.17062

Abstract

ChatGPT (Generative Pre-trained Transformer) is a natural language processing model based on Artificial Intelligence (AI) that is currently trending. ChatGPT is widely used by the public because it is considered very helpful in completing tasks or solving problems faced by society. However, as the use of ChatGPT grows, questions have arisen about how people perceive and respond to interactions with ChatGPT present. The use of ChatGPT not only creates opportunities but also new challenges in understanding user perceptions and sentiments toward this technology. For example, various controversies have emerged regarding the presence of ChatGPT. Therefore, this research aims to determine the sentiments of society, particularly among users of social media X, toward ChatGPT, and whether most of society views it positively, negatively, or neutrally. By conducting sentiment analysis and implementing Text Mining, the tendency of a particular sentiment or opinion, whether it leans toward positive, negative, or neutral, can be obtained relatively easily. The method used in this research is SEMMA (Sample, Explore, Modify, Model, Assess) with Naïve Bayes as the algorithm to be implemented. To evaluate the model, a Confusion Matrix is used. The sentiment analysis results show that out of a total of 1,314 data points, 39.4% were positive, 37.7% were neutral, and 22.9% were negative. The classification model achieved an accuracy of 72.78%, which is considered quite good.
Enhancing Urban Waste Management: An IoT-based Automated Trash Volume Monitoring System Ayu Latifah; Dede Kurniadi; Muhammad Sanusi
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.560

Abstract

Industrial development nowadays affects the increase in types of packaging waste which causes the accumulation of waste that has the potential to damage the environment. This research uses an Internet of Things for Automatic Waste Volume Monitoring System, so that waste management in an area can be improved. The purpose of this research is to make it easier for trashman to monitor the volume of the garbage collector on the notification feature. The research method used is the Rapid Application Development methodology with the Requirement Planning stage to analyze and identify the purpose of the system, then design the tools and system and create tools and system. Testing is used to evaluate the results of the tools and system. The result of the research is a prototype of an Internet of Things-based for Automatic Waste Volume Monitoring System tool equipped with a website-based monitoring system and for each user. Apart from that, the system which is equipped with a sensor that can detect the volume of the garbage collector is also equipped with an automatic opening and closing sensor to maintain the health of its users to provide answers to the problem of waste, especially in urban areas. Keywords: Internet of Things, Monitoring System, Waste, Waste Volume.
Natural Disaster Classification Using MobileNet with Transfer Learning Asri Mulyani; Dede Kurniadi; Gina Suciyana
Engineering Science Letter Vol. 5 No. 01 (2026): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001682

Abstract

Natural disasters are events caused by natural phenomena that cause massive damage and pose a threat to human safety. Based on EM-DAT data (2000–2025), there have been more than 10,000 global disasters, resulting in millions of casualties and trillions of US dollars in losses. Notably, 2024 saw US$320 billion in losses due to extreme weather. This condition emphasizes the importance of an accurate disaster classification system for mitigation and rapid response. This study aims to develop a natural disaster image classification model using the Convolutional Neural Network (CNN) method with a Transfer Learning approach using the MobileNetV2 architecture, which is known to be efficient and lightweight. This study employs the SEMMA (Sample, Explore, Modify, Model, Assess) methodology, beginning with sampling, which involves collecting image data from various open sources, such as Kaggle and previous literature. The data is then processed through the selection, cleaning, and normalization stages. Data exploration is conducted to understand class distribution and detect data imbalance. To overcome this problem, the Synthetic Minority Over-sampling Technique (SMOTE) and image data augmentation (such as rotation, flipping, and contrast adjustment) were used to enrich the training data variation. The pre-trained MobileNetV2 model was then retrained using the modified data, with adjustments to hyperparameters to achieve optimal performance. The evaluation was conducted using various metrics, namely accuracy, precision, recall, F1-score, confusion matrix, and AUC-ROC curve. The results demonstrate that the combination of Transfer Learning, data augmentation, and SMOTE can enhance model performance, achieving an accuracy of up to 99% and an AUC-ROC above 0.99 on public test data. Additionally, testing on 26 private test images yielded an accuracy of 92.31%, with 24 of the 26 images classified correctly. These findings confirm that the combination of MobileNetV2, augmentation, and SMOTE effectively improves multi-class classification performance on natural disaster images. Furthermore, the use of a relatively lightweight model makes this system more efficient to implement on devices with limited resources, thereby supporting disaster mitigation and rapid response efforts.
Performance-Efficiency Tradeoff Analysis of YOLOv8 Variants for Real-Time Multiclass Vehicle Detection in High-Density Traffic Dede Kurniadi; Asri Mulyani; Nuraisah Nuraisah
Engineering Science Letter Vol. 5 No. 01 (2026): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001702

Abstract

The growing number of vehicles in Indonesia increases the need for an efficient and reliable traffic monitoring system. In Garut Regency, traffic monitoring is still carried out manually without the support of artificial intelligence, thus limiting the effectiveness of real-time traffic analysis. This study develops and evaluates a CCTV image-based vehicle classification model using YOLOv8 with a focus on application in real-world traffic conditions. The development process follows the Machine Learning Life Cycle (MLLC) stages, including data acquisition, preprocessing, training, and model evaluation. The dataset comprises 1,200 CCTV traffic images from 10 locations in Garut Regency, supplemented by 7,426 additional images from the Roboflow platform to enhance the diversity of viewpoints and visual conditions. To address class imbalance, an undersampling technique is applied so that each vehicle category, motorcycle, car, truck, bus, and public transportation, has a balanced number of instances. Three YOLOv8 variants, namely Nano, Small, and Medium, are trained and evaluated using two testing schemes: a 70:20:10 data split and a 5-fold cross-validation method. Performance evaluation was conducted using the mean Average Precision (mAP), precision, recall, and inference speed metrics. The experimental results show that YOLOv8m with the 5-Fold Cross Validation scheme produces the best performance with mAP@50 of 0.947, precision of 0.932, and recall of 0.883, while YOLOv8n excels in terms of inference speed with an average of ±8.77 ms/frame. These findings suggest that the selection of YOLOv8 variants should consider the balance between accuracy and computational efficiency and confirm the potential of YOLOv8 as an initial component of an automated CCTV-based traffic monitoring system in real-world environments with limited resources.
IoT-Based Real-Time River Monitoring and Early Flood Warning Using ESP32 and HC-SR04 Asri Mulyani; Dede Kurniadi; Rizki Esa Saputra
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1252

Abstract

Floods remain one of the most frequent and destructive natural disasters in Indonesia, primarily caused by rising river water levels. This study presents a prototype of an IoT-based river water level monitoring system using an HC-SR04 ultrasonic sensor, an ESP32 microcontroller, and the Blynk platform for real-time monitoring and alerting. The system classifies water levels into three categories: safe, alert, and danger, triggering local alarms (buzzers) and smartphone notifications accordingly. Evaluation results show a high sensor accuracy of up to 99.7%, with a notification delay averaging 1.2 seconds and a buzzer response time of less than 0.5 seconds. The system has been tested through black box testing and field simulations and has shown reliable performance for early flood warnings. These findings demonstrate the system’s potential in enhancing flood preparedness, particularly in vulnerable communities. Future enhancements include deploying waterproof sensors and integrating additional alert platforms such as SMS or sirens.
Implementation of Machine Learning Model to Detect Sign Language Movement in SIBI Learning Media Leni Fitriani; Dede Kurniadi; Ilham Syahidatul Rajab
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1159

Abstract

This research focuses on the development of a web-based Indonesian Sign Language System (SIBI) learning application with motion detection to improve the precision of sign language practice. Despite the government's introduction of SIBI as an official system, existing platforms lack tools to validate the accuracy of hand movements. Using the Design Sprint methodology—comprising Understand, Define, Sketch, Decide, Prototype, and Validate phases—this study employs Microsoft Azure Machine Learning to create a motion detection model capable of recognizing SIBI gestures. The application offers an interactive learning experience, allowing users to practice and receive real-time feedback on their accuracy. Initial trials demonstrated high prediction accuracy, achieving 99.82% on public datasets and 96.4% on private datasets. Beta testing revealed an 86% satisfaction rate among users, indicating the application’s effectiveness in enhancing the learning process. By providing accessibility through standard web browsers and incorporating advanced motion detection, this application contributes to inclusivity, facilitating broader public understanding and interest in learning sign language.
Hospital Virtual Tour Website Design Using Multimedia Development Life Cycle Dede Kurniadi; Murni Lestari Rahmi; Nabila Putri Nurhaliza
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.3832

Abstract

In 2023, Indonesia recorded 3,155 hospitals spread across the country, comprising 2,636 general and 519 specialized hospitals. Although the number is significant, not all community members have easy, direct access to hospitals. To overcome this challenge, virtual tour technology has emerged as a relevant solution to facilitate access to information and increase the transparency of hospital services. This project aims to develop a virtual tour website for Medina Hospital in Garut Regency. The project uses a systematic development method known as the Multimedia Development Life Cycle (MDLC), which includes stages from concept to distribution. The resulting website allows users to explore various hospital areas, such as the Main Building, Emergency Room, Tulip Building, and Chemotherapy Poly, through a virtual 360-degree panoramic view. Additionally, building and floor selection features are designed to make it easy for users to navigate. This website is also equipped with a chatbot feature that helps users find the location of a specific room and video tutorial guides that provide instructions for using the website. The results of the black box test show that the website functions well without any significant technical problems, so it is ready for public use. This website is expected to increase accessibility and convenience for users in obtaining information about the facilities and rooms available at Medina Hospital.
RMSProp Optimizer and KAN Method-Based CNN on Rupiah Banknote Classification for Visually Impaired Dede Kurniadi; Murni Lestari Rahmi; Benedicto B. Balilo Jr; Hilmi Aulawi
Engineering Science Letter Vol. 4 No. 02 (2025): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.00936

Abstract

The visually impaired refers to individuals who experience a loss of visual function. Approximately 4 million people, or about 1.5% of Indonesia's total population, are visually impaired. They rely on their sense of touch to recognize banknote denominations in financial transactions. However, damaged banknotes often hinder identification and increase the risk of fraud. Therefore, this study aims to develop a rupiah banknote denomination classification model to assist them in conducting independent transactions. The researchers developed a CNN-KAN model with the RMSProp optimizer using a private dataset comprising 800 images of Rupiah banknotes with denominations of IDR 1,000, IDR 2,000, IDR 5,000, IDR 10,000, IDR 20,000, IDR 50,000, IDR 75,000, and IDR 100,000 from the 2016, 2020, and 2022 emission years. The dataset encompasses variations in image perspectives, lighting conditions, and the physical state of banknotes, including both intact and damaged ones, with up to 30% of the samples comprising damaged banknotes. Data augmentation techniques were implemented to improve data diversity. The dataset was then utilized for training and testing with different split ratios: 50:50, 60:40, 70:30, 80:20, and 90:10. Performance evaluation was conducted using loss, accuracy, precision, recall, and AUC-ROC metrics. Experimental results indicate that the CNN-KAN model with the RMSProp optimizer achieved optimal performance. In the 90:10 data split scenario, the model achieved 100% accuracy, precision, and recall, with an AUC-ROC of 1 and a loss of 0.008. Therefore, the CNN-KAN model with the RMSProp optimizer has been proven effective for implementing Rupiah banknote denomination detection for the visually impaired in an automated system.
Co-Authors A. Abdul Latif Abania, Nia Abdulah, Farhan Naufal Abdurrahman, Fauzan Abdussalam, Iqbal Abdussalam Abdusy Syakur Amin Ade Sutedi Ade Sutedi Ade Sutedi, Ade Adiwangsa, Alfian Akmal Agil Rahmat Agus Hermawan Agus Nugraha Agustiansyah, Yoga Ahmad Habib Lutfi Aisyah Fitri Islami Ajif, Arvin Muhammad Ajiz, Rafi Nurkholiq Akbar, Gugun Geusan Alamsyah, Renaldy Aldy Rialdy Atmadja Ali Djamhuri Alisha Fauzia, Fathia Alkamal, Chaerulsyah Alvin Zainal Musthafa Alwan Nul Hakim Amrulloh, Muhammad Fawaz Andri Saepuloh Aneu Suci Nurjanah Asri Indah Pertiwi Asri Mulyani Asri Rahayu Ningsih Ayu Latifah Ayu Suryani B. Balilo Jr , Benedicto B. Balilo Jr, Benedicto Balilo Jr, Benedicto B. Barlinti Maryam Benedicto B. Balilo Jr Budik Burhanuddin, Ridwan Cahya Mutiara Dede Sopiah Della Adelia Anugrah Detila Rostilawati Dewi Tresnawati Dhea Arynie Noor Annisa Diar Nur Rizky Diaz Radhian Salam Diazki, Moch Haiqal Diki Jaelani Dini Destiani Siti Fatimah Diva Nuratnika Rahayu Dudy Mohammad Arifin Dyka Afan Afthori Dzikri Nursyaban Efi Sofiah Elsen, Rickard Endang Prayoga Hidayatulloh Eri Satria Erick Fernando B311087192 Erwan Yani Erwan Yani, Erwan Erwin Gunadhi Rahayu, Raden Erwin Widianto Fadillah, Hadi Bagus Faisal, Ridwan Nur Fajar Rahman Faturrohman, Nadhif Fauziah, Fathia Alisha Fauziyah, Asyifa Fikri Zakaria Rahman Firmansyah, Marshal Fitri Nuraeni Fitriani, Ranti Fitriyani Gelar Panca Ginanjar Ghilman Hasbi Basith Gina Suciyana Gisna Fauzian Dermawan H. Bunyamin Hadi Wijaya, Tryana Haekal, Mohamad Fikri Hamzah Nurrifqi Fakhri Fikrillah Hari Ilham Nur Akbar Hasfi Syahrul Ramadhan Hazar, Aura Fitria Helmalia P, Nabilla Febriani Hendri Aji Pangestu Heri Johari Heri Suhendar Heri Suhendar Hilmi Aulawi Ida Farida Ikbal Lukmanul Hakim Ikhrom, Taufik Darul Ikmal Muhammad Fadhil Ilham Muhamad Ramdan Ilham Syahidatul Rajab Imas Dewi Ariyanti Inda Muliana Indra Trisna Raharja Indri Tri Julianto Indri Tri Julianto Intan Sri Fatmalasari Irawan, Muhammad Randy Irfan Qusaeri Irfanov, Muhammad Irsyad Ahmad Iskandar, Joko Jajang Jaenudin Jajang Romansyah Jembar, Tegar Hanafi Khaerunisa, Nisrina Khoerunisa, Sarah Kusmayadi, Kusmayadi Latifah, Ayu Leni Fitriani Leni Fitriani Leni Fitriani, Leni Lia Amelia Lindayani, Lindayani M. Mesa Fauzi Mahendra Akbar Musadad Maulana , Muhammad Arief Maulana, Ahmad Rakha Maulana, Ilham Ahmad Maulana, Yusep Maulina, Wina Senja Meta Regita Mochamad Deni Ramdani Muhamad Solihin Muhammad Abdul Yusup Hanifah Muhammad Affan Al Sidqi Muhammad Rikza Nashrulloh Muhammad Saleh Muhammad Sanusi Muhammad Sanusi Muhammad Wildan Muliana, Inda Murni Lestari Rahmi Muttaqin, Moch Riefky Chaerul Nabila Putri Nurhaliza Nita Nurliawati Nugraha, M Aldi Nugraha, Nikolas Pranata Nuraisah Nuraisah Nurfadillah, Rifa Sri Nurhaliza, Nabila Putri Nurlisina, Elisa Nurpatmah, Lisna Nursa'diah, Rifania Sapta Nursyaban, Dzikri Nurul Fauziah Nurul Khumaida Nurzaman, Muhammad Zein Omar Komarudin Pratama, Reifalga Gais Prayoga, Moch. Gumelar Putri, Mita Hidayani Raharja, Indra Trisna Rahayu, Diva Nuratnika Rahayu, Raden Erwin Gunadhi Rajab, Ilham Syahidatul Ramdhan, Dekha Ramdhani Hidayat Randy Wardan Ridwan Setiawan Ridwan Setiawan Ridwan Setiawan Ridwan Setiawan Rifky Muhammad Shidiq Rinda Cahyana Rinda Cahyana Risfiyanisa Fasha Rizki Esa Saputra Rizki Fauziah Roeri Fajri Firdaus Rohman, Fauza Rohmanto, Ricky Rostina Sundayana Rubi Setiawan Rudi Sutrio Safei P, M Iqbal Ismail Sarah Khoerunisa Sermana, Elsa Maharani Sheny Puspita Indriyani Siti Rima Fauziyah Sofwan Hamdan Fikri Sopiah, Dede Sri Intan Multajam Sri Mulyani Lestari SRI RAHAYU Sri Rahayu Sri Rahayu Syahrul Sidiq Syaiffani, Moch Assami Tina Maryana Undang Indrajaya W, Faksi Ahmad Wahidah, Tania Agusviani Wiwit Septiani Yanti Sofiyanti Yayat Supriatna Yoga Handoko Agustin Yosep Septiana Yosep Septiana Yuni Yuliani Yusfar Ilhaqul Choer Yusuf Mauluddin Zaqiah, Neng Nufus Zulkarnaen, Ade Iskandar