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All Journal TEKNIK INFORMATIKA Jurnal Ilmiah KOMPUTASI EDUMATIC: Jurnal Pendidikan Informatika Intellect : Indonesian Journal of Learning and Technological Innovation Coreid Journal INTERNAL (Information System Journal)
Cecep Nurul Alam, Cecep Nurul
Teknik Informatika Fakultas Sains dan Teknologi UIN SGD Bandung
Computer Science & IT Education Electrical & Electronics Engineering Other

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SELECTING NETWORK MONITORING SYSTEM SOFTWARE WITH ANALYTICAL HIERARCHY PROCESS METHOD Chotib, Ahmad Sulhi; Sobri, Abdul Muis; Alam, Cecep Nurul
JURNAL TEKNIK INFORMATIKA Vol 13, No 1 (2020): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.321 KB) | DOI: 10.15408/jti.v13i1.15981

Abstract

Nowadays there are lots of Network Monitoring System (NMS) software that are reliable and easy to use, such as CiscoWork, HP-OpenView, and IBM-Tivoli. It is just that the software is quite expensive, because it has classified as a commercial product. Fortunately, availability of NMS products is not limited to commercial, but also many other alternatives, namely products that classified as Free and Open Source Software (FOSS). Unfortunately, most of these FOSS products are not only difficult to implementation both in term of installation and configuration, but also limitations in the number of nodes and types of monitored, including network devices, servers, and applications. The method of selecting FOSS-based NMS software based on the Oyku Alanbay and ISO 7498-4 research methods through the Analytical Hierarchy Process (AHP) approach using the Expert Choice tool. Among GroundWork, Hyperic, Nagios, OpenNMS, and Zenoss, based on the results of this study put Nagios as a fair reliable software for monitoring networks, servers, and applications.
Implementasi Algoritma Support Vector Machine untuk Meingdentifikasi Komentar Negatif dalam Gambar di Media Sosial Andriyan, Acep Razif; Alam, Cecep Nurul; Sa’adillah, Dian; Maylawati, Maylawati; Irfan, Mohamad; Lukman, Nur
Intellect : Indonesian Journal of Learning and Technological Innovation Vol. 2 No. 1 (2023): Intellect : Indonesian Journal of Learning and Technological Innovation
Publisher : Yayasan Lembaga Studi Makwa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57255/intellect.v2i1.288

Abstract

As many as 191 million people are active users of social media in Indonesia, with many users often expressing opinions or making comments on social media that are positive or negative, such as blaspheming, bullying, insulting and so on. One form of comment is presented through images (memes), namely images that contain text in them. Therefore, a system was created to classify two types of images, positive and negative, using the SVM algorithm method with RBF kernel and OCR technology for retrieving text in images. The SVM algorithm functions to carry out classification and OCR technology functions to extract text from an image. Testing was carried out using split validation which produced the accuracy of the best model using a data comparison of 90:10 and produced an accuracy of 85.7%. Abstrak Sebanyak 191 juta orang sebagai pengguna aktif media sosial di indonesia, dengan banyaknya pengguna sering kali menyampaikan pendapat atau berkomentar di media sosial yang bersifat positif maupun negatif seperti menghujat, membuly, mencaci dan lain sebagainya. Salah satu bentuk komentar tersebut disajikan melalui gambar (meme) yaitu gambar yang mengandung teks di dalamnya. Maka dari itu diperlukan sebuah sistem untuk mengklasifikasi dua jenis gambar yang bersifat positif dan negatif menggunakan metode algoritma SVM dengan karnel RBF dan teknologi OCR untuk pengambilan teks dalam gambar. Algoritma SVM berfungsi untuk melakukan klasifikasi dan teknologi OCR berfungsi untuk mengekstrak text yang berada pada sebua gambar. Pengujian dilakukan dengan menggunakan split validation yang menghasilkan akurasi dari model terbaik dengan menggunakan perbandingan data 90:10 dan menghasilkan akurasi 85.7%.
Integrasi Kamus Multibahasa pada Feed Forward Neural Network dan IndoBERT dalam Pengembangan Chatbot Mobile Pamungkas, Arba Adhy; Alam, Cecep Nurul; Atmadja, Aldy Rialdy; Juliansyah, Roby
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27886

Abstract

The development of digital technology drives the need for efficient and responsive communication services that support multilingual. This study aims to develop a chatbot that facilitates communication and operational tasks for users of the DigiTeam application by integrating a multilingual dictionary into the Feed Forward Neural Network (FFNN) model and IndoBERT. The research method used is CRISP-DM, a systematic approach in data exploration, preparation, modeling, and implementation. The DigiTeam application was developed using the Agile methodology to gradually enhance the features and functionalities of the application. The dataset consists of 456 patterns and 106 tags containing common and operational work-related questions. This study utilizes a multilingual dictionary with 309 words to improve the chatbot's context understanding and response accuracy to user queries. The test results show that integrating the multilingual dictionary into the FFNN and IndoBERT models yields an accuracy of 95.45% with balanced precision and recall, demonstrating the chatbot's ability to understand and respond to multilingual queries in real-time, while also improving operational efficiency and information access in the workplace.
Implementation of Finite State Automata on e-Knows Telegram Chatbot Alam, Cecep Nurul; Firdaus, Imam
CoreID Journal Vol. 1 No. 1 (2023): March 2023
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v1i1.3

Abstract

The State Islamic University of Sunan Gunung Djati Bandung has a bold learning system called e-Knows. So far, if the user has a school, he must contact the admin manually. The problems are diverse, and several issues can bring personal impact. Automata language theory is the basic logic for mapping the telegram e-Knows chatbot system. The mapping is done by dividing each system using finite state automata to facilitate the completion of the system.
Deteksi Pneumonia pada Citra Akhir X – Ray Dada Menggunakan Convolutional Neural Networks Berdasarkan Fitur Prewitt Operator Raihan, Raihan; Alam, Cecep Nurul; Zulfikar, Wildan Budiawan
INTERNAL (Information System Journal) Vol. 8 No. 1 (2025)
Publisher : Masoem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/internal.v8i1.1380

Abstract

Pneumonia is a lung infection that is a leading cause of death, especially in children and adults in developing countries. The diagnosis of pneumonia is usually made through physical examination and interpretation of chest X-rays, but the results can vary depending on the experience of the doctor, potentially leading to misdiagnosis. This study uses a convolutional neural network (CNN) to detect pneumonia in X-ray images, with additional feature processing methods, such as the Prewitt operator to handle class imbalance. The goal is to improve the accuracy of pneumonia detection so that it can assist medical personnel in decision making and reduce misdiagnosis. As a result, the developed model achieved an accuracy of 96.59% on training data with consistent improvement, demonstrating the potential of CNN in supporting pneumonia diagnosis more accurately and reliably.
Peningkatan Keamanan On-Device Model Pada Aplikasi Offline Android Dengan AES dan Obfuskasi Setiawan, Munazir Dzuana; Alam, Cecep Nurul; Jumadi
Jurnal Ilmiah Komputasi Vol. 24 No. 2 (2025): Jurnal Ilmiah Komputasi : Vol. 24 No 2, Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.24.2.3781

Abstract

Metode on-device model menawarkan keunggulan seperti waktu respons yang cepat, hemat bandwidth, pengurangan biaya komputasi cloud. Metode on-device model pada aplikasi android memiliki kerentanan terhadap serangan reverse engineering atau decompiling, yang menyebabkan pencurian model sehingga dapat diakses tanpa izin. Model dalam tingkat produksi dianggap sebagai kekayaan intelektual. Oleh karena itu, diperlukan perlindungan untuk melindungi model AI dari akses yang tidak sah. Kombinasi antara kriptografi dan obfuskasi pada aplikasi android menjadi salah satu solusi dalam melindungi model kecerdasan buatan (AI) dari potensi eksploitasi, kriptografi berfungsi untuk melindungi model AI, serta obfuskasi untuk mengaburkan kode dan menyembunyikan kunci dekripsi model di dalam aplikasi Android. Penelitian ini menggunakan model MobileNet V1 dalam format Tensorflow Lite di enkripsi menggunakan algoritma AES (Advanced Encryption Standard). Hasil penelitian menunjukkan bahwa algoritma AES berfungsi dengan baik dan cepat dalam proses enkripsi maupun dekripsi pada aplikasi android. Penerapan obfuskasi pada aplikasi android terbukti efektif dalam menyulitkan dalam analisis kode aplikasi dan mencegah pengambilan kunci dekripsi. Pendekatan ini menawarkan perlindungan tambahan untuk memastikan implementasi AI di aplikasi android menjadi lebih aman.
Co-Authors Aldy Rialdy Atmadja Andriyan, Acep Razif Chotib, Ahmad Sulhi Firdaus, Imam Juliansyah, Roby Jumadi Maylawati, Maylawati Mohamad Irfan, Mohamad Nur Lukman Pamungkas, Arba Adhy Raihan Raihan, Raihan Sa’adillah, Dian Setiawan, Munazir Dzuana Sobri, Abdul Muis Wildan Budiawan Zulfikar