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Agus Harjoko
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INDONESIA
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Published by Universitas Gadjah Mada
ISSN : 19781520     EISSN : 24607258     DOI : https://doi.org/10.22146/ijccs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Indonesian Journal of Computing and Cybernetics Systems (IJCCS), a two times annually provides a forum for the full range of scholarly study . IJCCS focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and eveolutionary computation, so that more intelligent system can be built to industrial applications. The topics include but not limited to : fuzzy logic, neural network, genetic algorithm and evolutionary computation, hybrid systems, adaptation and learning systems, distributed intelligence systems, network systems, human interface, biologically inspired evolutionary system, artificial life and industrial applications. The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 476 Documents
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Prioritizing Drug Procurement Using ABC, VEN, EOQ And ROP Combination Susilo Romadhon; Retantyo Wardoyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.63486

Abstract

The availability of drugs is one of the things that must be considered because if there is a deficiency or excess it can cause loss or disruption in patient care. The process to procure drugs that are still being carried out with uncertain considerations will create scheduling irregularities, this will have an impact on inventory costs due to accumulated inventory in warehouses or the absence of these drugs.This study aims to produce a decision support system for drug procurement using a combination of ABC methods, VEN analysis, ROP and EOQ.The test results show that the system can provide 3 recommendations for decision makers with consideration of the results of the ABC and VEN matrices and procurement calculations based on EOQ and ROP. The result of calculating the total Inventory Cost in the case example of the orodine drug based on the pharmacy calculation is IDR 708,500 while the calculation using the Economic Order Quantity method is IDR 689,381 from the calculation results obtained a savings of IDR 19,119.
Sunspot Number Prediction Using Gated Recurrent Unit (GRU) Algorithm Unix Izyah Arfianti; Dian Candra Rini Novitasari; Nanang Widodo; Moh. Hafiyusholeh; Wika Dianita Utami
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.63676

Abstract

Sunspot is an area on photosphere layer which is dark-colored. Sunspot is very important to be researched because sunspot is affected by sunspot numbers, which present the level of solar activity. This research was conducted to make prediction on sunspot numbers using Gated Recurrent Unit (GRU) algorithm. The work principle of GRU is similar to Long short-term Memory (LSTM) method: the information from the previous memory is processed through two gates, that is update gate and reset gate, then the output generated will be input for the next process. The purpose of predicting sunspot numbers was to find out the information of sunspot numbers in the future, so that if there is a significant increase in sunspot numbers, it can inform other physical consequences that may be caused. The data used was the data of monthly sunspot numbers obtained from SILSO website. The data division and parameters used were based on the results of the trials resulted in the smallest MAPE value. The smallest MAPE value obtained from the prediction was 7.171% with 70% training data, 30% testing data, 150 hidden layer, 32 batch size, 100 learning rate drop. 
CPU and eGPU Support System Based on Naive Bayes Classification Mursyid Ardiansyah; Wahyu Hidayat; Ema Utami; Suwanto Raharjo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.63689

Abstract

Central Processing Unit (CPU) and External Graphics Processing Unit (eGPU) technology are known as overclocks which aim to make the device exceed the benchmarks set by the device maker. Until now there is no determination to rank the two hardware within certain limits such as hardware price range and year-by-year. Therefore, it is necessary to process the ranking of the hardware using Simple Additive Weighting (SAW) to obtain a ranking range and determine the weight per type of hardware analyzed. It can be classified using Naïve Bayes to determine results of criteria combination between two hardware to determine possible criteria into "not good" and "good". This classification used to determine probability criteria of choosing a combination of CPU and eGPU hardware. The results of this study are getting the best CPU and eGPU every year using SAW and then classifying it for pricing. In testing conducted on application of Naïve Bayes using 80% of training data has 2776 data and 20% of testing data has 695 data that will be tested for accuracy, precision, recall, and F1-score. For results of tests that have been carried out get 0.78 accuracy results, precision 1, Recall 0.764, and F1-Score 0.866.
Author Obfuscation on Indonesian News Articles Using Genetic Algorithms Rayhan Naufal Ramadhan; Yunita Sari; Aina Musdholifah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.64526

Abstract

Authorship attribution is a method for identifying the author of a text from a group of potential authors and can solve the anonymity of unknown authors. Such method threatens anyone’s privacy, especially those who wish to write anonymously. To address this issue, author obfuscation is proposed to modify a text to disguise its author.In this research, a genetic algorithm-based author obfuscation model was created to modify Indonesian news articles to avoid identification from authorship attribution while keeping its semantics. The model iteratively changed some words in the article using crossover and mutation techniques guided by a fitness function which involve identification probability and similarity to the original article.The model is evaluated based on safety, soundness, and sensibleness parameter. The model has good safety since it can reduce the given authorship attribution model's accuracy by 0.3018 but drops to 0.1179 when tested on different models. Its soundness is pretty good since the similarity of the modified to the original articles reaches 0.7817. The model obtained a score of 2.571 on a scale of 0 to 4 in terms of sensibleness which indicates that some articles are acceptable in terms of grammar, but not a few are messy.
Decision Support System For Determining Campus Promotion Media In New Student Admissions With Analytical Network Process And Regression Methods Nur Iman; Supriadi Sahibu; Abdul Latief Arda
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.64544

Abstract

Campus promotion is carried out in an effort to introduce the campus to the community, especially prospective new students. This effort was carried out as an action that was considered effective in recruiting new students. Various obstacles experienced by tertiary institutions in implementing campus promotion, namely the lack of need for supporting funds, limited human resources (HR), the right decision system for the selection of promotional media. This study analyzes the decision support system in selecting the right promotional media for campus promotion. The research objective is to assist campus management in selecting the right promotional media with a decision support system for determining the promotion media for new student admissions and determining the priority of the promotional media that will be used by private universities (PTS) in the city of Kendari. The sample in this study amounted to 40 respondents from 24 universities. The method used is the Analytical Network Process (ANP) method and the Regression method uses factor analysis. The results of the research analysis show that promotional media using the campus website has a number one rating, namely with a value of 26.2% while word of mouth has a second rating of 23.3%, then social media with a score of 23.1%, brochure 8.9%, print media and electronic media with a value of 6.2% and billboards have a value of 5.7%.
Hate Speech Detection in Indonesian Twitter using Contextual Embedding Approach Guntur Budi Herwanto; Annisa Maulida Ningtyas; I Gede Mujiyatna; Kurniawan Eka Nugraha; I Nyoman Prayana Trisna
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.64916

Abstract

Hate speech develops along with the rapid development of social media. Hate speech is often issued due to a lack of public awareness of the difference between criticism and statements that might contribute to this crime. Therefore, it is very important to do early detection of sentences that will be written before causing a criminal act due to public ignorance. In this paper, we use the advancement of deep neural networks to predict whether a sentence contains a hate speech and abusive tone. We demonstrate the robustness of different word and contextual embedding to represent the semantic of hate speech words. In addition, we use a document embedding representation via a recurrent neural networks with gated recurrent unit as the main architecture to provide richer representation. Compared to syntactic representation of the previous approach, the contextual embedding in our model proved to give a significant boost on the performance by a significant margin.
Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning Auliya Rahman Isnain; Jepi Supriyanto; Muhammad Pajar Kharisma
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.65176

Abstract

This research was conducted to apply the KNN (K-Nearest Neighbor) algorithm in conducting sentiment analysis of Twitter users on issues related to government policies regarding Online Learning. Research using Tweet data as much as 1825 Indonesian tweet data data were collected from February 1, 2020 to September 30, 2020. Using the python library, Tweepy. word weighting using TF-IDF, will be classified into two classes of sentiment values, positive and negative. After testing with K of 20, the highest accuracy results were obtained when K = 10 with an accuracy value of 84.65% with a precision of 87%, a recall of 86% f measure 87% and an error rate of 0.12% and a tendency was also obtained. public opinion on online learning tends to be positive.
Analysis of classic assumption test and multiple linear regression coefficient test for employee structural office recommendation Debby Alita; Ade Dwi Putra; Dedi Darwis
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.65586

Abstract

The performance appraisal process in Religious High Court Bandar Lampung has not been carried out objectively, but rather a subjectivity element (relationship closeness). Some employees occupy structural positions but do not fulfil competence and promotion principles, so that it has an impact on providing promotion to a position in the judiciary. Multiple Linear Regression method can provide a predictive model for employee recommendations entitled to occupy positions in the agency. The method implementation using SPSS produces an equation Y = 74.177 + 0.035X1 + 0.020X2 - 0.026X3 + 0.045X4 + 0.001X5. This equation is applied to the employee performance values, and it is obtained from 40 employees 26 employees deserve to be given recommendations promotion. Regression performance testing results using 10-cross validation get the correlation coefficient value is 80.66% with MAE value of 2.24% and RMSE 3.88%, which mean has good performance.
Online Learning Video Recommendation System Based on Course and Sylabus Using Content-Based Filtering Faisal Ramadhan; Aina Musdholifah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.65623

Abstract

Learning using video media such as watching videos on YouTube is an alternative method of learning that is often used. However, there are so many learning videos available that finding videos with the right content is difficult and time-consuming. Therefore, this study builds a recommendation system that can recommend videos based on courses and syllabus. The recommendation system works by looking for similarity between courses and syllabus with video annotations using the cosine similarity method. The video annotation is the title and description of the video captured in real-time from YouTube using the YouTube API. This recommendation system will produce recommendations in the form of five videos based on the selected courses and syllabus. The test results show that the average performance percentage is 81.13% in achieving the recommendation system goals, namely relevance, novelty, serendipity and increasing recommendation diversity.
Transliteration of Hiragana and Katakana Handwritten Characters Using CNN-SVM Nicolaus Euclides Wahyu Nugroho; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.66062

Abstract

Hiragana and katakana handwritten characters are often used when writing words in Japanese. Japanese itself is often used by native Japanese as well as people learning Japanese around the world. Hiragana and katakana characters themselves are difficult to learn because many characters are similar to one another. In this study, hiragana and basic katakana, dakuten, handakuten, and youon were used, which were taken from the respondents using a questionnaire. This study used the CNN method which will be compared with a combination of the CNN and SVM methods which have been designed to identify each character that has been prepared. Preprocessing of character images uses the methods of image resizing, grayscaling, binarization, dilation, and erosion. The preprocessed results will be input for CNN as a feature extraction tool and SVM as a tool for character recognition. The results of this study obtained accuracy with the following parameters: 69×69 image size, 3 patience values, val_loss monitor callbacks, Nadam optimization function, 0.001 learning rate value, 30 epochs value, and SVM RBF kernel. If using a system that only uses the CNN network, the accuracy is 87.82%. The results obtained when using a combination of CNN and SVM were 88.21%.

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