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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmu Komputer dan Agri-Informatika Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) CESS (Journal of Computer Engineering, System and Science) Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Petir Jurnal Ilmu Komputer dan Bisnis Indonesian Journal of Multidisciplinary Science Jurnal Sains Terapan : Wahana Informasi dan Alih Teknologi Pertanian Journal of Applied Agricultural Science and Technology Journal of Internet and Software Engineering Journal of Technology and System Information Scientific Journal of Informatics
Shelvie Nidya Neyman
Department Of Computer Science, IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Veterinary

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Identification of Android APK malware through local and global feature extraction using meta classifier Herawan, Yoga; Sitanggang, Imas Sukaesih; Neyman, Shelvie Nidya
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1834-1849

Abstract

Android, the most widely used mobile operating system, is also the most vulnerable to malware due to its high popularity. This has significantly focused on Android malware detection in mobile security. While extensive research has been conducted using various methods, new malware’s emergence underscores this field’s dynamic nature and the need for continuous research. The motivation that drives malware developers to create Android malware constantly is the potential to access Android devices, thereby gaining access to sensitive user information. This study, which is a complex and in-depth exploration, aims to detect Android malware using a meta-classifier that combines the single-classifier light gradient boosting machine, support vector machine, and random forest. The process involves converting disassembled malware codes into grey images for global and local feature extraction. The classification accuracy is 97% at best on a malware dataset of 3,963 samples. The main contribution of this paper is to produce an Android APK malware detector model that works by combining multiple machine learning algorithms trained using the dataset resulting from local and global feature extraction algorithms.
Deep Learning Approaches for Plant Disease Diagnosis Systems: A Review and Future Research Agendas Riyanto, Verry; Nurdiati, Sri; Marimin, Marimin; Syukur, Muhamad; Neyman, Shelvie Nidya
Journal of Applied Agricultural Science and Technology Vol. 9 No. 2 (2025): Journal of Applied Agricultural Science and Technology
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v9i2.308

Abstract

To identify novel advancements in plant diseases detection and classification systems employing Machine Learning (ML), Deep Learning (DL), and Transfer Learning (TL), this research compiled 111 peer-reviewed papers published between 2019 and early 2023. The literature was sourced from databases such as Scopus and Web of Science using keywords related to deep learning and leaf disease. A structured analysis of various plant disease classification models is presented through tables and graphics. This paper systematically reviews the model approaches employed, datasets utilized, countries involved, and the validation and evaluation methods applied in plant disease identification. Each algorithm is annotated with suitable processing techniques, such as image segmentation and feature extraction, along with standard experimental metrics, including the total number of training/testing datasets utilized, the quantity of disease images considered, and the classifier type employed. The findings of this study serve as a valuable resource for researchers seeking to identify specific plant diseases through a literature-based approach. Additionally, the implementation of mobile-based applications using the DL approach is expected to enhance agricultural productivity.
Strategi Pencegahan Efektif terhadap Serangan DDoS Slowloris menggunakan Kali Linux dan Linux Mint Ruswandi, Keinanjung; Pohan, Muhammad Reza Zulva; Halim, Kevin Viriya; Neyman, Shelvie Nidya
Journal of Technology and System Information Vol. 1 No. 4 (2024): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v1i4.2645

Abstract

Serangan Distributed Denial of Service (DDoS) telah menjadi ancaman serius dalam keamanan siber, dengan serangan tipe Slowloris menjadi salah satu yang paling merusak. Penelitian ini mengeksplorasi strategi pencegahan terhadap serangan Slowloris menggunakan pendekatan campuran yang melibatkan Kali Linux sebagai mesin serangan dan Linux Mint sebagai target. Data sekunder dari literatur digunakan untuk memahami serangan DDoS dan teknik pencegahannya, sementara data primer diperoleh melalui eksperimen praktis. Hasilnya menunjukkan bahwa langkah-langkah pencegahan, seperti peningkatan timeout, dapat efektif mengurangi dampak serangan Slowloris terhadap kinerja dan ketersediaan layanan pada Linux Mint. Analisis kuantitatif menunjukkan perbedaan yang signifikan dalam respons sistem sebelum dan sesudah implementasi langkah-langkah pencegahan. Penelitian ini memberikan wawasan penting dalam melindungi sistem mereka dari serangan DDoS.
Implementasi Firewall pada Linux untuk Pencegahan Dari Serangan DoS Tambunan, Muhammad Rafid Habibi; Neyman, Shelvie Nidya
Journal of Technology and System Information Vol. 1 No. 4 (2024): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v1i4.2648

Abstract

Serangan Denial of Service (DoS) merupakan ancaman serius bagi ketersediaan layanan jaringan yang dapat menyebabkan gangguan signifikan. Jurnal ini membahas implementasi firewall dan iptables sebagai solusi efektif untuk mengatasi serangan DoS. Dengan menggunakan firewall, lalu lintas jaringan yang mencurigakan dapat difilter dan akses yang tidak diinginkan dapat diblokir. Sementara itu, iptables memberikan kontrol granular untuk mengatur aturan kebijakan keamanan secara spesifik. Studi kasus dalam jurnal ini menunjukkan bahwa kombinasi firewall dan iptables secara signifikan mengurangi dampak serangan DoS terhadap jaringan. Evaluasi menunjukkan peningkatan kinerja dan stabilitas jaringan yang dilindungi dengan metode ini. Kesimpulan artikel menegaskan bahwa implementasi firewall dan iptables merupakan pendekatan yang efisien dalam memperkuat keamanan jaringan terhadap serangan DoS, dengan menggambarkan strategi yang dapat diadopsi oleh organisasi untuk meningkatkan ketahanan mereka terhadap ancaman tersebut. Dengan memperhatikan hasil evaluasi dan studi kasus yang disajikan, implementasi firewall dan iptables menjadi pilihan yang tepat bagi organisasi yang ingin meningkatkan keamanan jaringan mereka dan mengurangi risiko serangan DoS.
Pengaruh Serangan Slow HTTP DoS terhadap Layanan Web: Studi Eksperimental dengan Slowhttptest Safitrah, Tiara; Sinaga, Antonio Banggas Gregory; Alghifari, Muhammad; Neyman, Shelvie Nidya
Journal of Technology and System Information Vol. 1 No. 4 (2024): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v1i4.2663

Abstract

Penelitian ini bertujuan untuk menganalisis dampak serangan Denial of Service (DoS) terhadap performa dan ketersediaan layanan web, dengan menggunakan alamat domain hydrolevi.foxlust.my.id sebagai sampel pengujian. Topik ini dipilih karena serangan DoS dapat menyebabkan kerugian finansial dan kerusakan reputasi yang signifikan bagi pemilik situs web. Metode yang digunakan adalah pendekatan eksperimental melalui simulasi serangan menggunakan alat slowhttptest untuk mengukur respons server terhadap serangan Slow HTTP DoS. Hasil penelitian menunjukkan bahwa serangan DoS menyebabkan penurunan performa layanan web yang signifikan, memperlambat respons terhadap permintaan pengguna, dan meningkatkan risiko kesalahan sistem. Visualisasi menggunakan EtherApe mengindikasikan peningkatan lalu lintas jaringan yang berlebihan, sehingga layanan web tidak dapat diakses setelah serangan berjalan selama 171 detik. Hal ini menegaskan bahwa server tidak mampu menangani beban serangan tersebut. Oleh karena itu, sangat penting bagi pemilik server untuk menerapkan langkah-langkah pencegahan seperti peningkatan kapasitas server, implementasi solusi anti-DoS, dan penggunaan jaringan Content Delivery Network (CDN). Penelitian ini menekankan pentingnya kesiapsiagaan dan langkah-langkah mitigasi dalam menghadapi ancaman keamanan siber guna memastikan kelancaran layanan web.
Website Design for Providing Product Price Information of Micro, Small, and Medium Enterprises (MSMEs) Retail Business Sipahutar, Luat Paska; Findi, Muhammad; Neyman, Shelvie Nidya
Indonesian Journal of Multidisciplinary Science Vol. 4 No. 7 (2025): Indonesian Journal of Multidisciplinary Science
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/ijoms.v4i7.1122

Abstract

MSMEs dominate Indonesia’s economy, comprising 99.99% of business units, yet face challenges in digital adoption. This study addresses the gap by designing a website to streamline retail pricing information for Bekasi Regency’s MSMEs, leveraging rebranding and technology to enhance market reach. The research aims to develop a user-friendly website (www.produkumkmsejahtera.com) for price transparency, validate its effectiveness, and analyze MSME digital engagement. Conducted from November 2024 to January 2025, the research employed a mixed-method approach: surveys with 150 MSMEs, validity/reliability tests (Cronbach’s alpha 0.956), and iterative website development using CSS, HTML, JavaScript, and Laravel. Data collection included GPS-based price tracking and WhatsApp integration for customer interaction. The logo and responsive design significantly improved user engagement. Dominant sectors were crafts (weaving, leather) and food (presto milkfish). Validity tests confirmed robust metrics for information quality and trust. The study highlights the need for digital tools in MSME growth, recommending a future Play Store app with payment features. Findings support policymakers and MSMEs in adopting scalable digital solutions.
A Novel Approach for Bali Cattle Classification: Integrating the Fuzzy Inference System with Certainty Factor and Morphometric Parameters Arnaldy, Defiana; Seminar, Kudang Boro; Neyman, Shelvie Nidya; Sukoco, Heru; Muladno, -
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3218

Abstract

Enhancing the productivity and quality of Balinese cattle is a crucial goal for improving livestock management practices in Indonesia. Traditional evaluation methods used by farmers are often subjective and inconsistent, leading to inaccuracies in cattle classification and limiting the effectiveness of breeding and selection processes. To address these challenges, this study proposes a Fuzzy Inference System with Certainty Factor (FIS-CF) to improve cattle classification by providing more objective and reliable grading criteria. The model utilizes key physical parameters, including shoulder height, body length, and chest circumference, as input features to categorize cattle into three quality classes. A diverse dataset was collected from the People's Animal Husbandry School (SPR) and various farms across Indonesia to evaluate the model's performance. The FIS-CF model achieved a classification accuracy of 95.93% and a balanced accuracy of 96.20%, outperforming traditional methods that rely on subjective assessment. These results demonstrate that the proposed model provides a consistent, scalable, and data-driven solution for livestock classification, helping farmers make more informed decisions in cattle selection and breeding. Additionally, the model addresses key limitations of current practices by reducing reliance on manual evaluations, which often vary between assessors. The findings highlight the potential for wider adoption of the FIS-CF model across the livestock sector to improve productivity and streamline herd management processes. Future research will aim to refine the model further by incorporating additional parameters, such as age and weight, and expanding its validation to larger datasets covering different cattle breeds and farming environments to ensure broader applicability in sustainable livestock management.
Pengembangan Model Multilayer Classifier Menggunakan Metode Ensemble Learning untuk Grading Brokoli Imaduddin, Zaki; Purwanto, Yohanes Aris; Hartono Wijaya, Sony; Nidya Neyman, Shelvie
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 4: Agustus 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.124

Abstract

Permintaan brokoli di Indonesia terus meningkat 15% sampai dengan 20% per tahun. Sayangnya supply masih terbatas dan kualitas masih kurang. Untuk menentukan kualitas brokoli diperlukan adanya proses grading yaitu proses pemeringkat brokoli menjadi grade A, B dan C berdasarkan tiga parameter utama yaitu warna, ukuran, dan bentuk. Sayangnya, tidak semua petani memahami mengenai proses grading tersebut. Hal ini menyebabkan kerugian pada petani dan pengusaha brokoli. Penelitian ini bertujuan untuk mengembangkan algoritma grading menggunakan Convolusional neural network (CNN) berdasarkan 2 buah citra yaitu citra kepala brokoli dari tampak atas dan tampak samping. Dataset pada penelitian ini sebesar 600 data. Teknik yang digunakan ialah modifikasi beberapa model deep learning yaitu ResNet50, EfficientNetB2, VGG16 pada bagian layer klasifikasinya, lalu dibandingkan dengan hasil akurasi dari masing-masing outputnya. Penelitian ini juga menggunakan metode ensemble learning dimana menggunakan kombinasi dari 3 fitur berbeda. Fitur warna, ukuran dan bentuk digabungkan pada proses training dan testing untuk melakukan klasifikasi grade brokoli. Pada fase testing digunakan teknik voting untuk pengambilan keputusan grading. Akurasi terbaik ada pada model ResNet50 dengan hasil klasifikasi brokoli sebesar 90% yang didapatkan melalui penggunaan 5 dense layer pada layer klasifikasi, sehingga mampu melebihi hasil akurasi dari beberapa model deep learning lainnya. Algoritma dari penelitian ini menawarkan solusi grading yang lebih objektif dan konsisten dibandingkan sistem manual, sehingga petani dan pengusaha brokoli dalam meningkatkan efisiensi, mengurangi kerugian, dan memastikan kualitas produk yang lebih baik bagi konsumen.   Abstract The demand for broccoli in Indonesia has been increasing by 15% to 20% annually. However, supply remains limited, and quality control is inadequate. To assess broccoli quality, a grading process is required, classifying broccoli into Grades A, B, and C based on three primary parameters: color, size, and shape. Unfortunately, not all farmers possess sufficient knowledge of this grading process, leading to financial losses for both farmers and broccoli businesses. This study aims to develop a grading algorithm using a Convolutional Neural Network (CNN) based on two images, namely a top-view and a side-view image of a broccoli head. The dataset comprises 600 samples. The methodology involves modifying the classification layers of several deep learning models, namely ResNet50, EfficientNetB2, and VGG16, and comparing their classification accuracy. Additionally, an ensemble learning approach is employed, integrating three distinct features—color, size, and shape—into the training and testing phases for broccoli grading. The voting technique is utilized in the testing phase to enhance decision-making in the grading process. Experimental results indicate that the ResNet50 model achieves the highest classification accuracy at 90%, attributed to the incorporation of five dense layers in the classification stage. This performance surpasses that of other deep learning models. The proposed algorithm provides a more objective and consistent grading system compared to manual methods, enabling farmers and broccoli enterprises to enhance efficiency, reduce financial losses, and ensure higher product quality for consumers.  
Estimation Model of Nutritional Content Based on Broiler Feed Images Using Convolutional Neural Network and Random Forest Mufti, Abdul; Sitanggang, Imas Sukaesih; Neyman, Shelvie Nidya; Abdullah, Luki
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.28682

Abstract

Purpose: This research aims to develop an intelligent model to estimate the nutritional content of broiler chicken feed based on feed images to assist farmers in selecting the best broiler feed and quickly verifying its quality to meet requirements. Methods: The methodology of this research includes literature study, data collection, data preprocessing, image classification, model evaluation, integration of CNN and random forest models, and estimation of nutritional content based on feed images. We collected 99 samples of broiler chicken feed from online stores in various regions of Indonesia, particularly Java. Next, we took pictures with a smartphone and analyzed the nutritional content using near-infrared spectroscopy. Preprocess the data by enhancing the dataset (color space and data augmentation). We use Convolutional Neural Network (CNN) for the classification of broiler feed images. The performance of the CNN model is evaluated using a confusion matrix. We integrate CNN and Random Forest Regressor (RFR) to estimate nutritional content from the features of broiler feed images. Result: The performance evaluation shows that the CNN (VGG-16) model is 0.9744% accurate and the RFR model has the highest R2 value of 0.8018. The benefits of this research include faster, more efficient, and automated feed quality measurement compared to traditional methods; maintaining feed quality standards; and avoiding health risks for livestock. Novelty: This research introduces an intelligent model to estimates the nutritional content of broiler feed images by integrating a CNN model with an RFR.
Development of a Penetration Testing Framework for Identifying Security Vulnerability Solutions in WiFi Networks: Pengembangan Framewok Penetration Testing untuk Proses Pencarian Solusi Kerentanan Keamanan pada Jaringan Wifi Imran, Ali; Neyman, Shelvie Nidya; Rahmawan, Hendra
Telematika Vol 22 No 1 (2025): Edisi Februari 2025
Publisher : Jurusan Informatika

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

Abstract

The rapid increase in internet users has driven the development of WiFi networks, which play a crucial role in providing secure internet access, especially within Industry 4.0 and Industry 5.0 environments that rely on efficient data exchange. Penetration testing (pentest) is a vital approach for auditing and evaluating the security level of WiFi networks. Several frameworks such as PTES, PETA, and ISSAF are often used as references, although only a few are explicitly designed for WiFi networks. This study proposes a modification of the PTES framework to better align with the security characteristics of WiFi networks by providing relevant solution recommendations. The integration of the Boyer-Moore algorithm is employed as an efficient method to identify solutions for detected vulnerabilities. The implementation of this framework is demonstrated through testing the suggestion process, which produces solution recommendations based on vulnerabilities found during the pentest. The Boyer-Moore algorithm exhibits high efficiency in generating recommendations with a response time of 0.0000087 seconds.
Co-Authors A'isy, Nabil Arif Adi, Divo Wibowo Adzimi, Sista Naelly Affriza Brilyan Relo Pambudi Agus Putra Ahmad Rizaqu Muttaqi Al Habsy, Muhamad Alexandi, Muhammad Findi Alfasih, Hafiz Agi Alghifari, Muhammad Ali Djamhuri Ali Imran Alita Wulan Dini Ansyafa, Khairunnissa Zahran Athallah, Zaki Rafi Benhard Sitohang Defiana Arnaldy Eliyah Acantha Manapa Sampetoding Erydhani, Gama Batara Ezer, Angga Eben Fadhil, Muhamad Fajarudin, Muhammad Febrika Ardy, Lutfi Aziz Halim, Kevin Viriya Harahap, Handika Saputra Hartono Wijaya, Sony Hendra Rahmawan Hendra Rahmawan Herawan, Yoga Heru Sukoco Hesmi Aria Yanti I Nyoman Prama Pradnyana Imaduddin, Zaki Imas Sukaesih Sitanggang Irawan, Ferdiansyah Irman Hermadi Istiqomah, Iklima Kudang Boro Seminar Kurniawan, Hafiz Abdul Fatah Lindayati Lindayati Lindayati Lindayati Lubis, Mohammad Hafiz Fachrezi Luki Abdullah Manurung, Cindy Arditha Claudia Marimin Marimin Mayangkara, Mahatmadi Ariq mufti, abdul Muhamad Syukur Muhammad Fikri Isnaini Muladno - Nugroho, Arga Agung Pohan, Muhammad Reza Zulva Rahman, Allegra Alif Ramadhan, Fauzan Naufal Gibran Ruswandi, Keinanjung Safitrah, Tiara Setiawan, Aep Sinaga, Antonio Banggas Gregory Sipahutar, Luat Paska Sitorus, David Zico Rafael Sri Nurdiati Sugi Guritman Suraswati, Indah Tambunan, Muhammad Rafid Habibi Toto Haryanto Verry Riyanto Wahjuni, Sri Wijaya, Sony H Yan Mitha Djaksana Yanto, Nur Witdi