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All Journal Jurnal Pekommas Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JMECS (Journal of Measurements, Electronics, Communications, and Systems) Journal of Data Science and Its Applications Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Journal of Applied Data Sciences Journal of Informatics and Communication Technology (JICT) SOROT: Jurnal Pengabdian Kepada Masyarakat Journal of Informatics and Communication Technology (JICT)
Ade Rahmat Iskandar
Universitas Telkom
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Other

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Implementasi Virtual Private Network Pada Jaringan Multi Protocol Label Switching Traffic Engineering ade Nurhayati; Risnu Pradana Holiyastuta; Ade Rahmat Iskandar
Journal of Informatics and Communication Technology (JICT) Vol. 2 No. 1 (2020): Juni
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v2i1.47

Abstract

Along with the development of telecommunications technology, we need a network that provides traffic services to send data to distant places quickly but does not forget about security, some solutions are the Multi Protocol Label Switching method, Virtual Private Network, and Traffic Engineering. MPLS VPN TE is a combination of several methods that can be used for this problem. In this research, simulation is carried out using GNS3 software and the results of the analysis are carried out using a Wireshark software. This scenario uses the measurement load using FTP and HTTP. The measurement parameters of QoS are transfer time, throughput, delay, and packet loss ratio. The measurement results show that QoS value for transfer time of 25 MB file is 16.94s, and transfer time for HTTP is 4.2 s. The Throughput of 25 MB file is 1.606MB/s, and throughput for HTTP is 5.6 MB/s. The Delay of 25MB file is 1.22 ms, and delay HTTP is 105.37 ms
ONLINE COLABORATION SEBAGAI UPAYA MENINGKATKAN PRODUKTIFITAS KERJA DALAM MASA WORK FROM HOME (WFH) Ade Rahmat Iskandar; Hary Nugroho
Journal of Informatics and Communication Technology (JICT) Vol. 4 No. 1 (2022)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v4i1.124

Abstract

Dalam masa Lock down, banyak instansi-instansi pemerintah, perusahaan badan usaha milik negara (BUMN), maupun perusahaan swasta lainnya memberlakukan proses kerja yang dinamakan work from home (WFH) atau bekerja dari rumah. Penelitian ini dilaksanakan dari tanggal 16 Maret samapai dengan 17 April 2020 bagi para pegawai Akademi Telkom selama masa bekerja dari rumah, untuk menghindari penyebaran virus Corona atau covid-19 yang sedang melanda di Indonesia dan negara-negara lainnya. Metode yang dilakukan dalam penelitian ini adalah explorasi terhadap beberapa pola kerja WFH menggunakan beberapa tipe online colaboration. Luaran dari penelitian ini adalah penggunaan media Whatsapp, Zoom dan wfh’s website yang paling banyak digunakan selama masa bekerja dari rumah bagi para pegawai Akademi Telkom Jakarta.
Perbandingan Akurasi Prediksi Kerusakan CPU Antara Teknik Certainty Factor dan Forward Chaining Laksana, Tri Ginanjar; Iskandar, Ade Rahmat; Ahmad, Wan Nooraishya Wan
Jurnal Pekommas Vol 9 No 1 (2024): Juni 2024
Publisher : Sekolah Tinggi Multi Media “MMTC” Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jpkm.v9i1.5531

Abstract

The CPU plays a vital role in determining the performance of a computer system in contemporary computing. If the CPU sustains damage, it may result in significant interruption to the computer's functioning. This study presents a computational technique that aims to enhance the accuracy of CPU damage predictions. The system utilises fundamental knowledge of damage diagnosis and is validated via evaluating 11 early damage symptoms that are often seen. The Certainty Factor and Forward Chaining approaches ascertain CPU damage by quantifying the degree of truth in the expert's opinion conclusions via a comparison of the symptoms of harm. The second algorithm assesses the confidence level in a development by considering the weight assigned to the system by two parties: the user and the expert. The suggested algorithm yields the mean accuracy of the certainty factor approach in diagnosing computer damage utilising the constructed system. The diagnostic system has a precision rate of 84.9%, indicating that 9 out of 10 diagnoses made by the system align with those made by an expert. Next, the outcomes of the forward chaining algorithm test. All questions about symptoms were answered affirmatively, except for one test which had a negative response. A total of 39 diagnoses were obtained, with an average value of 82.9%. The study findings indicate that the suggested confidence factor method is more suited for use in embedded systems or web-based applications, however it is constrained by low processing.
Sistem Pakar Diagnosis Penyakit Ikan Gurami (Osphronemus Goramy) Menggunakan Case Based Reasoning Saraswati, Adinda Rahmi; Saintika, Yudha; Thohari, Afandi Nur Aziz; Iskandar, Ade Rahmat
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 4: Agustus 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020701953

Abstract

Ikan Gurami (Osphronemus Goramy) merupakan ikan yang banyak dibudidayakan dan dikomsumsi masyarakat ini menjadi sektor unggulan di beberapa wilayah kabupaten Banyumas. Ikan gurami yang dibudidayakan oleh masyarakat Banyumas, sebenarnya bukan tanpa hambatan. Salah satu hambatan bagi peternak gurami adalah penyakit yang disebabkan oleh bakteri. Pada penelitian ini penulis membuat sistem pakar untuk mendiagnosis penyakit ikan Gurami yang disebabkan bakteri. Penelitian ini menggunakan metode Case Based Reasoning dan Similarity Nearest Neighbor untuk mendapatkan solusi yang terbaik dari kasus yang di identifikasi. Metode tersebut membandingkan antara kasus lama dengan kasus baru dan menghitung suatu nilai similarity kasus. Nilai similarity tertinggi dapat dijadikan kesimpulan untuk kasus yang paling mirip dengan diagnosa pakar. Sehingga dari kedua metode tersebut dapat dihasilkan sistem pakar yang dapat mendiagnosis dan menganalisis sesuai dengan nilai kemiripan gejala terhadap penyakit, serta menampilkan solusi penanganan dari penyakit yang didiagnosis. Hasil pengujian antar kasus dan sistem menggunakan perhitungan similarity mencapai nilai terbaik yaitu 100%. Hasil pengujian akurasi sistem untuk diagnosis yang sesuai dengan pikiran pakar, memperoleh hasil sebesar 93,33% dari 30 kasus yang diuji dengan sistem. Kesimpulan dari hasil tersebut adalah sistem dapat dikatakan layak untuk mendiagnosis penyakit Gurami yang disebabkan bakteri sesuai dengan yang dipikirkan pakar. AbstractGurami (Osphronemus Goramy) is a fish that is widely cultivated and consumed by the community. This fish is a leading sector in several regions of Banyumas district. Gouramy which is cultivated by the Banyumas people, is actually not without obstacles. One obstacle for gouramy breeders is a disease caused by bacteria. Reporting from the online news portal, circulating in February 2018 circulated that news about Gurami farmers was losing money because thousands of broodstock fish that had been raised to death were attacked by bacterial diseases, namely Aeromoniasis. Experts who handle this are limited, namely only 2 people in the Banyumas Regency. In this study the authors made an expert system to diagnose Gurami fish disease caused by bacteria. This study uses the Case Based Reasoning (CBR) and Nearest Neighbor methods used to get the best solution from the identified case. The CBR method compares the old case with the new case and calculates a case similarity value. The system was built with 13 symptoms and 3 Gurami diseases caused by bacteria. Each symptom each has a weight of 5, 3, and 1. The highest similarity value can be used as a conclusion for the case most similar to the expert diagnosis. So that from these two methods an expert system can be produced that can diagnose and analyze according to the similarity of symptoms to the disease, as well as display solutions to the treatment of diagnosed diseases. The test results are between cases and the system uses the similarity calculation to achieve the best value of 100%. The results of the system accuracy test for diagnoses that are in accordance with the expert's mind, obtained results of 93.33% from 30 cases tested with the system. The conclusion of these results is that the system can be said to be feasible to diagnose Gurami disease caused by bacteria according to what experts think.
Simultaneous Hydroponic Nutrient Control Automation System Based on Internet of Things Adidrana, Demi; Iskandar, Ade Rahmat; Nurhayati, Ade; Suyatno, -; Ramdhani, Mohamad; Adam, Kharisma Bani; Ardianto, Rizki; Ekaputri, Cahyantari
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.865

Abstract

Hydroponic is one of the solutions of gardening methods using water as a nutrition medium. Usually, maintaining hydroponic plant quality and water nutrients are done manually and require human efforts, such as the degree of acidity or wetness (pH), TDS (Total Dissolved Solids), and nutrient temperature. With the Internet of Things technology, we can automate hydroponic control by measuring the nutrients' TDS, pH, and temperature values and controlling water nutrition by pump nutrition needs for hydroponic plants. This research uses the NFT (Nutrient Film Technique) for the hydroponic system and uses lettuce as the nutrition parameter. The lettuce parameters are pH, TDS, and Water Temperature equal to the sensor we used in the proposed IoT system. The condition has 27 classifications, and we use this classification as a reference in decision-making, using the K-Nearest Neighbor (KNN) algorithm to activate the actuator. We improve the simultaneous actuator from previous research with specified intervals and duration to achieve ideal nutritional conditions. The other improvement is that we collect more data and more testing times. The accuracy was 91.2%, with k = 3. From the evaluation results, the accuracy of KNN is quite high and has an advantage, which has better accuracy than the other algorithms and can activate actuator simultaneously. We conclude that the hydroponic nutrient automation system using the Internet of Things method is ready for real planting use with this improvement.
Time Series Forecasting of Environmental Dynamics in Urban Ecotourism Forest Using Deep Learning Ade Rahmat Iskandar; Arif Imam Suroso; Irman Hermadi; Lilik Budi Prasetyo
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1029

Abstract

Time Series Forecasting of Environmental Dynamics in urban forests is quite challenging, unless new approaches such as deep learning and remote sensing are employed. Deep learning-based time series algorithms offer robust scientific capabilities for forecasting and assessing sustainability trends using sequential data. Among these, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) have gained widespread adoption across various predictive modeling domains. In the present research, these algorithms are employed to analyze urban forest raster data derived from the Srengseng Ecotourism Forest, located in West Jakarta, Indonesia. The present study focuses on predicting the temporal patterns of key spatial indicators: Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Forest Cover Density (FCD) in the Srengseng urban ecotourism forest area, spanning the years 2014 to 2024, through the application of LSTM, GRU, and BiLSTM deep learning architectures. The methodology used in this study is a combined approach involving remote sensing and deep learning. Spatial data were acquired through the delineation of a high-precision polygon of Srengseng Urban Forest using Google Earth Pro and Google Earth Engine (GEE). GeoTIFF datasets of NDVI, LST, and FCD for the years 2014–2024 were processed using Python-based modeling scripts. Model performance was evaluated through a comparative analysis of LSTM, GRU, and BiLSTM in predicting temporal trends in these ecological indicators. The results of this study show that the Bidirectional LSTM (BiLSTM) consistently demonstrated superior performance to predict all the data spatially, with scores of 0.94 for NDVI, 0.90 for FCD, and 0.85 for LST. Followed by LSTM that predicts NDVI (0.87), FCD (0.89), LST (0.83), as well as GRU, which can estimate spatial data NDVI (0.86), FCD (0.89), and LST (0.85). These results outperformed the predictive accuracy of both the standard LSTM and GRU models.
Pengembangan Solusi Berbasis Pwa Untuk Keterlibatan Orang Tua Pada Aktifitas Siswa Desi Nurnaningsih; Widang Muttaqin; Zuki Pristiantoro Putro; Ade Rahmat Iskandar; Aurellia Fira Artanti; Daniswara Rafi Pandora; Gading Ainunzaky Widianto
SOROT : Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 1 (2026): Januari
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/31s8p756

Abstract

SMA PU Al Bayyan Cibadak, Sukabumi, menghadapi tantangan signifikan dalam komunikasi sekolah-orang tua akibat ketergantungan pada metode tradisional seperti buku laporan fisik dan grup chat WhatsApp. Sistem ini terbukti kurang efektif karena sering menyebabkan informasi yang tidak akurat, tertunda, atau terlewat, sehingga menghambat peran supportive orang tua dalam proses pendidikan siswa. Untuk mengatasi masalah ini, penelitian mengusulkan implementasi Progressive Web App (PWA) AKRAB sebagai platform komunikasi terpadu. PWA dipilih karena keunggulan teknisnya yang memungkinkan akses mudah tanpa instalasi melalui browser, keamanan data yang terjamin melalui protokol HTTPS dan service workers, serta efisiensi biaya pengembangan dan pemeliharaan. Metode implementasi dirancang melalui empat tahap utama: analisis kebutuhan stakeholder, desain antarmuka yang user-friendly, uji coba pilot dengan pengguna terpilih, dan program pelatihan berkelanjutan. Platform ini akan menyediakan fitur terintegrasi untuk memantau kehadiran, jadwal kegiatan, pencapaian akademik, dan pengumuman sekolah secara real-time. Hasil yang diharapkan termasuk peningkatan transparansi informasi, efisiensi proses administrasi, dan peningkatan keterlibatan orang tua yang berdasarkan literatur berkorelasi positif dengan peningkatan hasil belajar siswa. Implementasi AKRAB tidak hanya menjadi solusi teknologi tetapi juga berpotensi memberdayakan komunitas orang tua dan menjadi model replicable untuk sekolah lain yang menghadapi tantangan komunikasi yang serupa.
Streaming Telemetry-Based Network Microburst Detection with Queue Metric Analytics for Packet Loss Mitigation Muchamad Rusdan; Ade Rahmat Iskandar
JMECS (Journal of Measurements, Electronics, Communications, and Systems) In Press Papers
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v13i1.10203

Abstract

Network microbursts, defined as high-intensity traffic surges lasting for an extremely short duration 100–800µѕ, have become a major cause of hidden packet loss that degrades the performance of critical applications in modern data center infrastructure. Conventional monitoring methods based on SNMP (Simple Network Management Protocol) fail to detect this transient phenomenon due to inadequate polling intervals, creating a blind spot in network visibility. This research designs, implements, and evaluates a microburst detection framework that leverages streaming telemetry and multivariate analysis of queue metrics to overcome the limitations of existing systems. The study adopts the Design Science Research (DSR) approach in a Mininet emulation environment with Open vSwitch, utilizing an integrated pipeline of gNMI/gRPC, Prometheus, and a Python-based detection algorithm that combines dynamic thresholding and queue metric correlation analysis. Evaluation against 1.6 million microburst events revealed that the proposed framework achieved a detection accuracy of 96.8% with an equivalent F1-Score at a 10 ms sampling interval, dramatically outperforming SNMP, which failed to detect any events. Correlation analysis showed a strong relationship between queue depth and packet drop rate, confirming the effectiveness of queue metrics as predictive indicators. The multivariate algorithm successfully reduced the false positive rate by 63% (from 5.7% to 2.1%) compared to a static threshold approach, despite increasing CPU overhead by 8–19%. The results of the study demonstrate the effectiveness of streaming telemetry with queue metric analysis for real-time microburst detection, while also providing practical implementation guidelines in the form of optimal configurations at 10–30ms intervals for various deployment scenarios.
A Deep Learning NLP Framework with Directional Augmentation for Cybersecurity Compliance Monitoring Tri Ginanjar Laksana; Prima Dina Atika; Asep Ramdhani Mahbub; Ade Rahmat Iskandar; Wan Nooraishy Wan Ahmad
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 2 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i2.28533

Abstract

Background: The increasing complexity of cybersecurity mandates, such as BSSN and Kominfo regulations, presents a significant challenge for automated compliance auditing in Indonesia. Traditional NLP models often struggle with the semantic gap between formal regulatory language and raw technical system telemetry, leading to high false-negative rates in security monitoring. Objective: The purpose of this research to develop a robust deep learning framework to automate cybersecurity compliance assessments while addressing the linguistic challenges of the Indonesian regulatory landscape. The primary goal is to enhance the detection of non-compliant system behaviors by bridging the gap between documentation and real-time logs. Methods: The proposed framework utilized a Transformer-based BERT architecture integrated with a novel Directional Augmentation (DA) mechanism. The methodology follows a four-phase process: (1) data collection of 8,240 labeled points, (2) an Anti-Leak Grouping Strategy to prevent data memorization, (3) implementation of DA through Semantic Polarization and Technical Jargon Injection, and (4) model training and evaluation.  Result: The findings of this research are indicate that the proposed framework significantly outperformed the baseline BERT model. Test Accuracy rose from 90.15% to 95.72%, while Validation Accuracy improved from 91.20% to 96.88%. The final model achieved an Overall Accuracy of 94.39%, maintaining a balanced F1-score and effectively reducing False Negatives to only 32 cases in the detection of security violations, Conlussion : Integrating Directional Augmentation into BERT optimizes Indonesian cybersecurity auditing by synchronizing BSSN/Kominfo regulatory language with technical telemetry through semantic polarization and jargon injection.
Co-Authors - Suyatno Ade Nurhayati, Ade Afandi Nur Aziz Thohari, Afandi Nur Aziz Ahmad Fauzi Maryadi Ahmad, Wan Nooraishya Wan Apri Junaidi, Apri Ardianto, Rizki Ari Purno Arif Imam Suroso Asep Herman Asep Ramdhani Mahbub Aurellia Fira Artanti Cahyantari Ekaputri Daniswara Rafi Pandora Demi Adidrana Desi Nurnaningsih Gading Ainunzaky Widianto Hary Nugroho Irman Hermadi Kharisma Bani Adam Lilik Budi Prasetyo Mohamad Ramdhani Muttaqin, Widang Prima Dina Atika Risnu Pradana Holiyastuta Rusdan, Muchamad Saraswati, Adinda Rahmi Tri Ginanjar Laksana Tri Ginanjar Laksana Wan Nooraishy Wan Ahmad Yudha Saintika Zuki Pristiantoro Putro