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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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INDONESIA
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 6 Documents
 1 
Search results for , issue "Vol 5, No 2 (2023): September 2023" : 6 Documents clear
Data Integrity Testing of Digital Evidence Data Capture Results on Private Cloud Computing Services Komarudin, Arif Maulana; Widiyasono, Nur; Aldya, Aldy Putra; Rizal, Randi
Innovation in Research of Informatics (Innovatics) 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.v5i2.8420

Abstract

Private Cloud has better advantages than other cloud services because private cloud is managed and run by the company itself so that cloud needs can be tailored to the company's needs, but allows abuse from within the company itself, as in the case study simulation of a Gojek startup company. This case occurred because of a security weakness in the system so that internal people took advantage of these weaknesses for their own benefit by leaking confidential data, acquisitions were carried out to prove and find evidence of crime, acquisitions used live acquisition techniques, namely acquisitions on an ongoing system, namely monitoring network traffic using Wireshark tools , the method in this case uses the Digital Forensics Investigating Framework (DFIF), data integrity must be properly maintained when acquiring digital evidence because to maintain the authenticity of the digital evidence obtained, then data integrity is tested on the digital evidence obtained, testing is carried out on digital evidence before and after the acquisition to see if there is a change in data integrity, the research results show that there is no change in data integrity.
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.
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.
Implementation of Data Mining at Laboratory Vocational High School Using The C4.5 Algorithm to Predict Students Major Preferences Suherman, Nurisya Rahma; Ruuhwan, Ruuhwan; Sudiarjo, Aso
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.v5i2.8479

Abstract

Education or the learning process is the primary thing for human life. Therefore, a place for acquiring knowledge is established, which is called a school. Schools have their own levels, ranging from early childhood education to higher education institutions. When students enter high school, they are required to make decisions in choosing their majors. Accompanied by technological advancements, the issues in high school major selection can be effectively and efficiently addressed using data mining. Common issues that usually arise include lack of accuracy, precision, and requiring a significant amount of time. Hence, the issues within major selection necessitate the use of data mining, employing the C4.5 algorithm method, to determine the accuracy and precision of large datasets. This research achieved with RapidMiner the result is accuracy score of 94.44%, precision of 81.37%, and sensitivity of 74.00%. Additionally, it also generated a decision tree and with Python has an accuracy of 93% because it automatically rounds the values, so there is no significant difference between the two tools. This proves that the C4.5 algorithm produces fairly accurate performance.
Unveiling Culinary Patterns: Implementation Of K-Means Clustering Algorithm on Food Products in Cafes Gumelar, Lasmi Lasmini; Ruuhwan, Ruuhwan; Hikmatyar, Missi
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.v5i2.8665

Abstract

Barcode Se'i and Coffee is one of the cafes on JL. Major Utarya, No. 48, Empangsari, District. Tawang, Tasikmalaya. Barcode Se'i and Coffee is quite famous because the concept of the place is nice, comfortable, and instagrammable. Not only that, but the Barcode café was also the first to create cow sei in Tasikmalaya. By analyzing the cafe menu groupings, information can be found regarding the level of menu sales. This type of analysis, capable of assessing sales levels, involves the use of data mining techniques such as clustering. Data mining is a data processing stage that aims to identify and extract patterns from a certain set of data. One of the methods included in data mining is the clustering technique. Reclassification techniques are used to group objects into several groups based on observed indicators, ensuring that all objects have a significant level of similarity compared to objects placed in different groups. With Rapidminer software and using the k-means algorithm with sales data for 11 months with the calculations carried out producing 5 clusters. Based on the comparison results of 3 K-Means algorithms with different K values, namely 3, 4, 5, the result from Davies Bouldin with a value close to 0 is a value with K 5, with the result from Davies Bouldin being - 0.912.

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