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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 5 Documents
 1 
Search results for , issue "Vol 4, No 2 (2022): September 2022" : 5 Documents clear
Perancangan Sistem Pendukung Keputusan Berbasis Website Pada Pemilihan Instrumen Investasi Terbaik Menggunakan Metode TOPSIS Surono, Anang; Rossena, Bayu Adjie; Kurniawati, Ika
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 4, No 2 (2022): September 2022
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v4i2.5286

Abstract

In managing finances there are various ways that can be chosen, such as saving and investing. Investment has long-term and short-term goals and has several types of instruments with each instrument having a promising return. Generation Z is now familiar with matters related to financial technology for their money management field. The lack of literacy among Generation Z in terms of investment raises doubts and even fear in making an investment making it difficult for them to choose or decide on the best investment instrument product. This study aims to help potential investors, especially Generation Z, in choosing the type of investment according to the selection criteria offered, namely Equity Mutual Funds, Money Market Mutual Funds, Crypto, Bonds, and Deposits. The research method used is Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Topsis is a multicriteria decision making method based on the concept that the best alternative not only has the shortest distance from the positive ideal solution but also has the longest distance from the negative ideal solution. The results of data analysis obtained the best preference values, namely Equity Mutual Funds of 0.75 and Deposits with a value of 0.62. The results of this study are implemented by designing a website-based decision support system using the PHP programming language and MySql database which can help determine the best investment instrument according to user preferences.  keywords : Decision Support System, Invesment, MySQL, PHP, TOPSIS
Naïve Bayes dan Support Vector Machine Berbasis PSO untuk Seleksi Fitur pada Sentiment Analysis Nugraha, Ahmad Fio
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 4, No 2 (2022): September 2022
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v4i2.5291

Abstract

Sentiment analysis is a process that aims to determine the content of the dataset in the form of positive, negative and neutral text. Currently the opinion of the general public is an important source of decision making. Social media is a place to express public opinion on an object, problem or event. Such as the government's policy regarding the relocation of the capital city of Indonesia, which was originally in Jakarta to Kalimantan, did not escape the attention of the public, especially Twitter users. One of the problems in sentiment analysis is the high number of attributes and dimensions in the dataset. In this study, sentiment analysis was carried out on the relocation of the national capital using the Naïve Bayes method, and the Support Vector Machine based on Particle Swarm Optimization (PSO). The advantages of the Support Vector Machine are High dimensional space and Vector document space. Feature selection greatly affects the performance of the classification, the use of PSO as feature selection to improve accuracy. The results of this study obtained the best accuracy value of 96.45% and the AUC value of 0.920 from the application of PSO on the Support Vector Machine. This result has increased when compared to the experimental results using Naïve Bayes and Support Vector Machine without PSO. The application of PSO on the Support Vector Machine is proven to get better accuracy results in predicting sentiment analysis on the dataset of moving the State Capital.
Implementasi Algoritma Apriori Dalam Menemukan Association Rules Pada Persediaan Sparepart Motor Nurhidayanti, Dina; Kurniawati, Ika
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 4, No 2 (2022): September 2022
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v4i2.5300

Abstract

Saat ini kompetisi bisnis dalam beragam bidang menjadi sangat erat. Selain pada bidang fashion, hidangan, dan elektronik, bidang pelayanan jasa dan perdagangan pun semakin melambung tinggi, seperti halnya bidang otomotif. Honda Tanabang Motor memiliki banyak data transaksi yang tidak dimanfaatkan untuk menganalisa persediaan barang yang laku atau banyak diminati pelanggan, serta tidak dimanfaatkan menjadi informasi yang berguna untuk strategi marketing. Penulis mencoba mengimplementasi algoritma apriori pada persediaan sparepart, karena algoritma apriori adalah metode yang paling tepat dalam mencari aturan asosiasi barang dan frequent-itemset. Hasil dari pengujian algoritma apriori telah ditemukan 3 (tiga) association rules yakni jika membeli Piece Set Slide maka membeli Busi, jika membeli Lampu Belakang maka membeli Kampas Rem Depan, dan jika membeli Lampu Depan maka membeli Kampas Rem Depan, dengan Nilai Confidence 75% dan Nilai Support 25%. Dengan hadirnya Implementasi Algoritma Apriori, diharapkan menjadi salah satu solusi untuk mempermudah perusahaan agar melakukan pengaturan ulang tata letak sparepart secara berdekatan untuk memudahkan dalam mengambil barang yang akan dikeluarkan, melakukan monitoring terhadap persediaan barang, hubungan antar tiap produk yang dibeli secara bersamaan dan penunjang informasi dalam pemesanan stok barang serta dapat membantu merumuskan strategi pemasaran untuk meningkatkan penjualan.   Kata Kunci : Apriori,  Aturan Asosiasi, Data Mining, Persediaan Barang 
Analisa Dan Perancangan Sistem Pendukung Keputusan Penentuan Promosi Jabatan Pada PT. XYZ Menggunakan Metode Profile Matching Sari, Mawar
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 4, No 2 (2022): September 2022
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v4i2.5303

Abstract

Human Resources (HR) is one of the important functions in an organization or company to get the best employees to achieve the goals of the company. The best way how to improve Human resources is by promoting or awarding the best employees. The selection of the best employees is required an objective system. PT. XYZ is one of company who implementing these methods to improve their resources, but the selecting are still conventional methods which final decision still determined under leadership company according to the existing delegation of authority. This research is to analyze and design a decision support system to determining the promotion using Profile Matching Method. the implementation will be two main aspect that will be assessed, they are Performance Aspects and work behavior aspect, each aspect has 5 sub-criteria. The final value of two aspects is calculated with different percentage which are 70% for the performance aspect and 30% for work behavior aspect. The results, there were 3 employees with the highest grades who entitled to be promoted, are the employee with NIK INDO09, INDO07 and INDO06 which score of 4,81, 44,62 and 4,49. Finally the decision support system implemented to the web-based application which expected to assist related parties in determining employee promotions based on specified criteria to get the objective decisions.
Jaringan Syaraf Tiruan Mendeteksi Penyakit Pneumonia Infeksi Saluran Pernafasan Akut Dengan Algoritma Backpropagation Edwar, Yulia; Rendy, Rendy; Sanoto, Jazuli
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 4, No 2 (2022): September 2022
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v4i2.5161

Abstract

Acute Respiratory Infections (ARI) is a health problem that often affects children and adults. For adults, acute respiratory infections are mild or common, but in children under five, this disease is a threat that can cause death. One of the causes of death due to acute respiratory infections is the incorrect diagnosis. This study aims to determine the level of accuracy and optimal neural network architecture in detecting ARI using the backpropagation method. The backpropagation method is a pattern recognition technique that is able to provide decision results based on trained data. This research was implemented using MATLAB software with several forms of network architecture. Symptoms of ARI that were used as input for detection of the disease consisted of 13 variables targeting non-pneumonia and pneumonia ARDs. Based on the research results, the architecture with the best configuration consists of 13 input layer neurons, 20 hidden layer neurons and 2 output layer neurons with a binary sigmoid activation function (logsig), a learning rate value of 0.5, an error tolerance value of 0.001, a maximum of epoch of 216 and MSE 0.000997. Artificial neural networks with the backpropagation method used for weight adjustment can respond to training data and testing data well, marked by the resulting network accuracy 100% in accordance with the desired target.

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