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Alam Rahmatulloh
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Program Studi Informatika Fakultas Teknik Universitas Siliwangi Jl. Siliwangi No. 24 Tasikmalaya, Jawa Barat
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Innovation in Research of Informatics (INNOVATICS)
Published by Universitas Siliwangi
ISSN : -     EISSN : 26568993     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Innovation in Research of Informatics (Innovatics) merupakan Jurnal Informatika yang bertujuan untuk mengembangkan penelitian di bidang: Machine Learning Computer Vision Internet of Things Information System and Technology Natural Language Processing Image Processing Network Security Geographic Information System Knowledge based Computer Graphic Cyber Security IT Governance Data Mining Game Development Digital Forensic Business Intelligence Pattern Recognization Virtual & Augmented Reality Virtualization Enterprise Application Self-Adaptive Systems Human Computer Interaction Cloud Computing Mobile Application Innovatics adalah jurnal peer-review yang ditulis dalam bahasa Indonesia yang diterbitkan dua kali dalam setahun mulai dari Vol. 1 No.1 Maret 2019 (Maret, dan September) dengan proses peninjauan menggunakan double-blind review.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 93 Documents
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Air Quality Classification Using Extreme Gradient Boosting (XGBOOST) Algorithm Sapari, Albi Mulyadi; Hadiana, Asep Id; Umbara, Fajri Rakhmat
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 5, No 2 (2023): September 2023
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v5i2.8444

Abstract

Air pollution is a serious issue caused by vehicle exhaust, industrial factories, and piles of garbage. The impact is detrimental to human health and the environment. To quickly and accurately monitor classification, techniques are used. One efficient and accurate classification algorithm is XGBoost, a development of the Gradient Decision Tree (GDBT) with several advantages, such as high scalability and prevention of overfitting. The parameters used in the classification include (PM10), (PM2,5),(SO2),(CO),(O3) and (NO2). This study aims to classify air quality into three labels or categories: good, moderate, and unhealthy. In the dataset used to experience an imbalance class, to overcome the imbalance class, techniques will be carried out, namely SMOTE, Random UnderSampling, and Random OverSampling, by producing an accuracy of up to 98,61% with the SMOTE technique for class imbalance. Testing the level of accuracy is done by using the Confusion Matrix.
Action Recommendation Model Development For Hydromon Application Using Deep Neural Network (DNN) Method Praseptiawan, Mugi; Athalla, Muhammad Nadhif; Untoro, Meida Cahyo
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 5, No 2 (2023): September 2023
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v5i2.8422

Abstract

Controlling hydroponic plants, which is currently being carried out manually, can be said to be less effective because it still involves the hard work of farmers to continuously monitor the condition of the hydroponic plants. Therefore, the general objective of this research is to develop a model that can be used as a recommendation system for actions that farmers need to take based on hydroponic crop conditions. The model formed with this machine learning method will then be used in the Hydromon application which allows farmers to manage and monitor the condition of hydroponic plants and take action based on the recommendations given. This model was developed using a deep neural network algorithm consisting of five layers with the help of the TensorFlow framework. The results show that the model is accurate with an accuracy value of 96.47% on the test data to classify plant conditions so that it can be used in the Hydromon application.
An Analysis of Classification Method Performance on Handwritten Lontara Numerals Bustam, Faida Daeng; Purnawansyah, Purnawansyah; Azis, Huzain
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11999

Abstract

The research investigates the performance of various classification methods on handwritten Lontara digits, a script used by the Bugis and Makassar communities in South Sulawesi, Indonesia. The dataset comprises 10,890 samples from 99 individuals, categorized into 10 classes (digits 0-9). The study employs the K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Nu-Support Vector Classifier (NuSVC) algorithms, implementing cross-validation to assess accuracy, precision, recall, and F1 score. The results indicate varying performance across classifiers, with GNB showing the highest recall, while KNN and NuSVC display moderate effectiveness. The study concludes with recommendations for further improving classification accuracy through enhanced feature extraction and algorithm optimization.
Comparison of Machine Learning Algorithms in Detecting Contaminants in Drinkable Water Elmeftahi, Souhayla; Rakhman, Maulana Decky; Rahmatulloh, Alam
Innovation in Research of Informatics (Innovatics) Vol 6, No 1 (2024): March 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i1.10385

Abstract

Water, a vital natural resource essential for human existence, is a fundamental human right, indispensable for a dignified life. Despite its significance, the quality of water is often compromised by a myriad of harmful substances, minerals, and contaminants stemming from various sectors like industry, agriculture, residential, and energy. Traditional methods such as WQI and STORET, relying on manual inspection, prove time-consuming. Thus, the integration of machine learning emerges as a pivotal solution to swiftly assess water quality.Numerous studies have explored this challenge using various algorithms; however, a definitive comparison is elusive due to the abundance of existing methods. In response, this research undertakes a meticulous evaluation of seven algorithms to ascertain the optimal approach for water quality classification, employing metric values as benchmarks. Notably, the Random Forest algorithm emerges as the most effective, achieving an impressive accuracy of approximately 84.8%. Following closely are the XGBoost and CatBoost algorithms, showcasing commendable performance with accuracies of 82.9% and 80.2%, respectively. Subsequent rankings include the Decision Tree algorithm at 77.3%, SVM at 72.3%, K-NN at 70.6%, and AdaBoost with the lowest accuracy at 63.33%. This comparative analysis contributes valuable insights for informed decision-making in water quality assessment.    
Naive Bayes and Wordcloud for Sentiment Analysis of Halal Tourism in Lombok Island Indonesia Irvandi, Irvandi; Irawan, Bambang; Nurdiawan, Odi
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 5, No 1 (2023): Maret 2023
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v5i1.6675

Abstract

Lombok is one of the halal tourist destinations in Indonesia and has been recognized by the world. To examine these assumptions based on sources from tourist opinion, it is necessary to carry out a sentiment analysis whether their presence is as expected. Google Maps is a platform that can show the location of the island of Lombok along with written reviews from tourists who have visited. The collection of review data is done through the Web Scraping technique on the Google Maps Review, then the data is processed using RapidMiner. The algorithm used is Naive Bayes, an algorithm that uses probability or the concept of opportunity in classification. A word cloud visualization is also displayed to bring up words that tourists often say. 1493 data were obtained after Web scraping and cleansing had been labeled with positive and negative sentiment categories. Preprocessing is carried out which includes tokenize, filter token by length, transform case, stopword, and stemming, then classification using the Naive Bayes algorithm. From the results of testing the Naive Bayes algorithm model, an accuracy rate of 74.75%. Word Cloud visualization also found the top words included "indah", "wisata". “pantai”, “alam”, “gunung”, and “masjid”.
Sarcasm Detection: A Comparative Analysis of RoBERTa-CNN vs RoBERTa-RNN Architectures Pawestri, Sheraton; Murinto, Murinto; Auzan, Muhammad
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11921

Abstract

Increasingly advanced technology and the creation of social media and the internet can become a forum for people to express things or opinions. However, comments or views from users sometimes contain sarcasm making it more difficult to understand. News headlines, sometimes contain sarcasm which makes readers confused about the content of the news. Therefore, in this research, a model was created for sarcasm detection. Many methods are used for sarcasm detection, but performance still needs to be improved. So this research aims to compare the performance of two text classification methods, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), in detecting sarcasm in English news headlines using RoBERTa text transformation.  RoBERTa produces a fixed-size vector of numbers 1x768. The research results show that CNN has better performance than RNN. CNN achieved the highest average accuracy of 0.891, precision of 0.878, recall of 0.874, and f1-score of 0.876, with a loss of 0.260 and a processing time of 508.1 milliseconds per epoch. On the contrary, RNN shows an accuracy of 0.711, precision of 0.692, recall of 0.620, f1-score 0.654, and loss of 0.564, with a longer processing time of 116500 milliseconds per epoch. The 10-fold cross-validation evaluation method ensures the model performs well and avoids overfitting. So it is recommended to use the combination of RoBERTa and CNN in other text classification applications that require high speed and accuracy. Further research is recommended to explore deeper CNN architectures or other architectural variations such as Transformer-based models for performance improvements.
Development of Network Security Using A Suricata-Based Intrusion Prevention System Againts Distributed Denial of Service Tahir, Muhlis; Wahyuningsih, Umami; Putra Pratama, Muhammad Iyan; Effindi, Muhamad Afif
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11187

Abstract

Network security is essential in today's rapid technological developments, especially to avoid undesirable things such as attacks carried out by irresponsible parties. An intrusion prevention system is one of the methods used in a network security system. One attack that causes weak server services is Distributed Denial of Service (DDoS). This research aims to develop a Suricata-based Intrusion Prevention System for network security at the research location and to carry out tests to prevent attacks on the network at the research location. This research uses a waterfall model consisting of 5 stages: Analysis, Design, Implementation, Testing and Maintenance. The results of the research carried out on the development of a Suricata-based Intrusion Prevention System were able to detect DDoS attacks (Syn Flood and Ping of Death) and block access to these attacks so that network traffic was stable by utilizing the firewall feature, namely Iptables. The Suricata-based Intrusion Prevention System (IPS) demonstrated strong performance in detecting DDoS attacks, with a 98% detection rate for Syn Flood attacks and a 95% detection rate for Ping of Death attacks. The system maintained an overall average detection rate of 96.5% across both attack types, while keeping false positives low, at 2% for Syn Flood and 3% for Ping of Death. This resulted in an overall false positive rate of 2.5%, indicating the IPS's effectiveness in accurately identifying threats with minimal erroneous alerts, thereby providing robust network security.
Decision Support System for Determining Employee Bonuses using Analytical Hierarchy Process Yuliyanti, Siti; Sartika, Tika
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 5, No 2 (2023): September 2023
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v5i2.8782

Abstract

Determining employee bonus salaries is one of the problems faced by every company, especially PT. Pyridam Farma, where the company finds it difficult to determine employees who are eligible to receive bonus salaries. There are many factors that cause this, including the fact that it takes quite a long time and the possibility of data being lost because it is still in hard copy form. This research uses the Analytical Hierarchy Process (AHP) as a weighting method for the basic criteria in determining employees who deserve a bonus salary, including length of service, absenteeism (attendance rate) and employee performance. The decision support system application determines employees who are eligible to receive this bonus salary on a web basis. The system that is built is able to determine which employees will receive bonus salaries based on predetermined weights and is able to determine what percentage of bonus salary employees will receive based on the assessments that have been carried out. This system can provide a solution for PT. Pyridam Farma to determine which employees will receive bonus salaries.
Enhancing Maintenance Efficiency Through K-Means Clustering at PT Semen Indonesia Alviano, Muhammad Fadhil; Alifah, Amalia Nur; Ardhani, Calista Ghea; Raditya, Helga Fadhil; Larasati, Harashta Tatimma
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.12520

Abstract

PT Semen Indonesia, an industrial company based in Gresik, East Java, is committed to enhancing operational efficiency and managing maintenance costs effectively. By analyzing patterns in maintenance frequency, total costs, and maintenance duration across their various plants, the company can identify work units that require more intensive attention or that can be optimized for greater efficiency. To achieve this, PT Semen Indonesia employs K-Means clustering analysis to gain deeper insights into the maintenance data, identifying patterns that can help improve operational efficiency and develop more targeted maintenance strategies based on the identified clusters. The clustering of planner groups is carried out using variables such as the number of maintenance activities, total costs, and duration of maintenance tasks. As a result of the K-Means clustering, the planner groups have been divided into two clusters: Cluster 1, which consists of planner groups that perform more efficiently, and Cluster 2, which includes those with less efficient performance. Based on these clustering results, PT Semen Indonesia should conduct further evaluation or review of the planner groups in Cluster 2.
An Algorithm for Color-Based Password Authentication to Increase Security Level Selamat, Siti Rahayu; Cai, Soung Young; Hassan, Nor Hafeizah; Yusof, Robiah
Innovation in Research of Informatics (Innovatics) Vol 6, No 1 (2024): March 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i1.10396

Abstract

Security level in authentication is essential to decrease the possibility of an account being guessed. Several authentication methods are widely used nowadays, covering digital aspects such as passwords, challenge-response, public and private key / digital certificates, and physical elements such as fingerprints, iris, or retina scanning. This paper aims to focus on solving the problem of the password. This textual authentication consists of many vulnerabilities open to attacks like eavesdropping, dictionary attack, and brute force attack by increasing the level of complexity in the authentication algorithm. In this paper, we proposed a new color-based password authentication algorithm to solve the vulnerabilities in textual authentication. The color-based password authentication algorithm consists of three main processes: color selection, hexadecimal password encryption, and password verification. This research contributes to a new color-based authentication by increasing the complexity of the verification process that can solve the vulnerabilities of textual authentication and harden the level of security in the authentication layer. This color-based authentication algorithm could fully replace textual authentication in the future and is worth using in sensitive data domains such as medical and health or banking institutions.

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