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PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic
Published by Universitas Islam 45 Bekasi
ISSN : 23033304     EISSN : 26203553     DOI : https://doi.org/10.33558/piksel
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal PIKSEL diterbitkan oleh Universitas Islam 45 Bekasi untuk mewadahi hasil penelitian di bidang komputer dan informatika. Jurnal ini pertama kali diterbitkan pada tahun 2013 dengan masa terbit 2 kali dalam setahun yaitu pada bulan Januari dan September. Mulai tahun 2014, Jurnal PIKSEL mengalami perubahan masa terbit yaitu setiap bulan Maret dan September namun tetap open access tanpa biaya publikasi. p-ISSN: 2303-3304, e-ISSN: 2620-3553. Available Online Since 2018.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 304 Documents
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Sentiment Analysis of On-Demand Ride-Hailing Systems using Support Vector Machine and Naïve Bayes Bhagaskara Farhan Wiguna; Herlawati Herlawati; Ajif Yunizar Pratama Yusuf
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7384

Abstract

Gojek is one of Indonesia's most popular online transportation, founded in 2010. The Gojek application has been downloaded one hundred forty-two million times with more than two million drivers and four hundred thousand partners in food delivery services. Due to the increasing use of the Gojek application and the importance of knowing user views about the services provided by the application. In this research, the sentiment analysis is using Support Vector Machine and the Naïve Bayes method to classify positive sentiment and negative sentiment. The target label focus on positive and negative labels to aims avoid the bias that exists in neutrally labeled reviews on the Gojek Application. The research process includes data collection, pre-processing the data, weighting with Term Frequency-Invers Document Frequency, Support Vector Machine, and Naïve Bayes training by dividing the data into 90% training data and 10% testing data and then evaluating the results using a confusion matrix. The results of testing using the Support Vector Machine algorithm resulted in 90% accuracy, 94% recall, 91% precision, and 94% f1-score, therefore the Naïve Bayes algorithm produces 77% accuracy, 96% recall, 77% precision, and 85% f1-score.
Classifying Half-Unemployment Levels in Indonesian Provinces: A K-Means Approach for Informed Policy Decisions Suhardjono Suhardjono; Hari Sugiarto; Dewi Yuliandari; Adjat Sudradjat; Luthfia Rohimah
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7390

Abstract

Half-level unemployment refers to individuals who work part-time and are not fully employed. Increasing the half-poverty rate from year to year can lead to challenges in the lives of these individuals. The issue arising with the rise in the half-poverty rate is the government's difficulty in prioritizing areas that require intervention to address these problems. Consequently, an increase in the half-poverty rate can have adverse consequences. Therefore, it is necessary to categorize underemployment rate data obtained from public sources, specifically from data.go.id, using the widely recognized clustering method known as K-Means. The purpose of this categorization is to identify and classify provinces with a significant prevalence of half-poverty levels. This classification will assist the government in making informed decisions when addressing individuals who meet the half-poverty criteria. The results were obtained by grouping the data from the first to the eighteenth iteration into three categories: 'large' (C1), 'medium' (C2), and 'small' (C3) in terms of half-poverty levels. Group C1 comprises 17 provinces with a high half-poverty rate, while C2 includes only 2 provinces, and C3 covers 16 provinces with a significant half-poverty rate. Based on these findings, it is advisable for the Indonesian government to consider implementing policies aimed at reducing the poverty level by half. Priority should especially be given to the C1 group when creating employment opportunities for the province's residents
Machine Learning-Based Classification for Scholarship Selection Asriyanik Asriyanik; Agung Pambudi
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7393

Abstract

University of Muhammadiyah Sukabumi (UMMI) is a university that accepts KIP scholarship every year. However, KIP student applicants always exceed the quota, so it requires a re-selection process to determine KIP Shcolarship Awardee. UMMI does not have a clear method to support decisions in the selection process for KIP Shcholarship Awardee. To solve this problem, a classification modeling process will be carried out from previous data using machine learning algorithms, namely with Decision Tree (DT) and Support Vector Machine (SVM) algorithms. The general method for its development uses the SEMMA method (Sample, Explore, Modify, Model, Assess). Starting with collecting a dataset of KIP recipients studying at UMMI from 2021-2022 which amounted to 519 data with 16 attributes. From the results of exploration, the main attributes that became features for modeling were DTKS Status, P3KE Status, Combined income of father and mother and achievement. These attributes are converted into numeric data for easy data modeling. The results of K-Fold Cross-Validation for the DT model in the case of classification of KIP Kuliah recipients resulted in an accuracy of 78.44% of the entire test dataset, a precision of 0.73107 indicating that 73.11% of the model's predictions were correct, recall (sensitivity level) of 78.45% and an F1 score of 73.20%. The results of modeling and validation with SVM are 80.17% accuracy, 84.44% precision and 80.17% recall. The SVM model shows slightly better in terms of accuracy and precision, both models show competitive performance in classifying KIP scholarship recipients studying at UMMI.
Optimization of Village Budget Plan Selection Based on Priorities Using Method Promethee and Borda Dwi Yanti Laily; Muhammad Dedi Irawan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7890

Abstract

The rapid information technology that is happening now has a significant impact on human life, including various activities that occur at the village office. The village head is responsible for managing funds at the village level including the collection and accountability of funds in accordance with the provisions of law no.33 of 2004 concerning the balance of central and regional governments. This study aims to implement a decision support system by combining two methods, namely the promethee and borda methods in selecting village budget plans in Tegal Sari village based on priority. The method used in this research is the Research and Development (R&D) method. The method used for calculations is the PROMETHEE and Borda methods. The PROMETHEE method is used to manage individual decisions from each decision maker, while the Borda method is used to manage group decisions resulting from the PROMETHEE ranking method. The criteria in this study are Cost, Completion Time, Impact on Regions/Society, Profits from Sustainable Investment, Factors of Interest. The results obtained from this research are Pemb. Health Sarpras as an alternative priority for the village budget.
PENERAPAN FUZZY INFERENCE SYSTEM MAMDANI UNTUK MENGUKUR TINGKAT KENYAMANAN PENGUNJUNG Julaiwa, Siti Hawa; Herlawati, Herlawati; Maimunah, Maimunah
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 4 No. 1 (2016): Maret 2016
Publisher : LPPM Universitas Islam 45 Bekasi

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Abstract

ABSTRACT This research is intended to determine the comfort level of visitors in order to improve comfort quality by knowing prioritized attributes to be maintained and improved by Mekarsari Fruit Garden Bogor. The method used is Fuzzy Inference System using max-min method who have membership degree min 0 and max 1 and followed by defuzzification. There are three criterias used that is variable service, facilities and cleanliness. Each variable has an indicators and the indicators used is 13 includes five questions on the performance variables, five questions on the variable facilities and 3 questions on cleanliness variables. Based on defuzzification obtained sample that categorized give good ratings to the convenience of visitors with score 9.5 and include good category. Keywords : Fuzzy inference system , the comfort level of visitors, Mamdani ABSTRAK Penelitian ini bermaksud mengetahui tingkat kenyamanan pengunjung agar dapat meningkatkan kualitas kenyamanan dan mengetahui atribut-atribut yang diprioritaskan untuk dipertahankan dan diperbaiki oleh Taman Buah Mekarsari Bogor. Metode yang digunakan penulis adalah Fuzzy Inference System dengan menggunakan metode max-min yang memiliki derajat keanggotaan min 0 dan max 1 dan dilanjutkan dengan defuzzifikasi. Terdapat 3 kriteria yang digunakan yaitu variabel pelayanan, fasilitas dan kebersihan. Tiap-tiap variabel memiliki indikator dan jumlah indikator yang digunakan sebanyak 13 meliputi 5 pertanyaan pada variabel Pelayanan, 5 pertanyaan pada variabel fasilitas dan 3 pertanyaan pada variabel kebersihan. Berdasarkan proses defuzzifikasi diperoleh sample yang dikategorikan memberikan penilaian baik terhadap kenyamanan pengunjung dengan nilai 9,5 dan masuk pada kategori baik. Kata Kunci : Fuzzy inference system, tingkat kenyamanan pengunjung, Mamdani
PENERAPAN MICOROSOFT EXCEL PADA METODE KUANTITATIF BISNIS DENGAN ANALYTICAL HIERARCHY PROCESS (PROSES ANALITIS HIERARKIS) Herlawati, Herlawati
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 1 No. 1 (2013): Januari 2013
Publisher : LPPM Universitas Islam 45 Bekasi

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Abstract

ABSTRAK Pengamatan mendasar tentang sifat manusia, pemikiran analitik, dan pengukuran membawa pada pengembangan suatu model yang berguna untuk memecahkan persoalan secara kuantitatif. Analytical Hierarchy Process (AHP) merupakan suatu model yang luwes yang mampu memberikan kesempatan bagi perorangan atau kelompok untuk membangun gagasan-gagasan dan mendefinisikan persoalan dengan cara membuat asumsi mereka masing-masing dan memperoleh pemecahan yang diinginkan darinya. Proses ini juga memungkinkan orang menguji kepekaan hasilnya terhadap perubahan informasi. Beberapa keuntungan penggunaan metode AHP adalah kesatuan, kompleksitas, saling ketergantungan, penyusunan hierarki, pengukuran, konsistensi, síntesis, tawar menawar, penilaian dan konsensus serta pengulangan proses. AHP merupakan salah satu tools dalam pemecahan masalah yang bersifat strategis, dalam hal ini digunakan software Microsoft Excel. Kata Kunci : AHP, kuantitatif, hierarki, excel ABSTRACT Basic research about human characteristic, especially in analythical thinking and measurement bring to a model that usefull for solving quatitativ problems. Analytical Heararchy Proces (AHP) is a model that flexibly can give the opportunity for individual or community to build the idea and define a problem with their assumption. This process also suitable for someone to give a test in regards to sensitivity of information change. Some benefits of AHP are: integrity, complexity, inter dependency, hierarchy position of information, consistency, synthesis, bargaining, checking and consensus, and process iterative. AHP is a tool for solving strategic problem that using Microsoft Excel in this paper. Keyword : AHP, quantitative, hierarchy, excel
KLASIFIKASI DINAMIS DENGAN MODIFIKASI ALGORITMA FUZZY C-MEANS (FCM) Herlawati, Herlawati; Handayanto, Rahmadya Trias
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 1 No. 2 (2013): September 2013
Publisher : LPPM Universitas Islam 45 Bekasi

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Abstract

ABSTRACTAside forecasting, classification is an important process in the data mining field. Nowdays, theclassification usually use soft computing algorithms, such as Fuzzy Inference System (FIS), NeuralNetworks (NNs), and Genetic Algorithms (GAs). Different from K-Means, the fuzzy-based classification issometimes is said soft clustering. Some dynamic method has been research using K-Means for obtaining theoptimal number of cluster. This paper try to implement this method for FCM algoritms because thisalgorithms run better than K-Means. Similar to Dynamic Clustering using K-Means, for FCM everyelements of cluster are counted the distance from the center. Key Workds : Fuzzy C-Means Clustering (FCM), Cluster Quality, Dynamic Classification ABSTRAKSelain peramalan, klasifikasi merupakan salah satu proses penting dalam bidang data mining. Saat iniklasifikasi banyak dilakukan dengan algoritma-algoritma yang berbasis soft computing seperti fuzzy,jaringan syaraf tiruan (JST) ataupun algoritma genetik. Berbeda dengan K-Means, klasifikasi berbasis fuzzyyang sering disebut fuzzy C-Means (FCM) merupakan klasifikasi halus (soft clustering). Beberapa metodedinamis dengan memodifikasi algoritma K-Means telah banyak dilakukan dan terbukti memiliki hasil yangoptimal. Tulisan ini bermaksud menerapkan metode dinamis itu pada algoritma FCM mengingat FCMmemiliki keunggulan tertentu dibanding K-Means. Seperti pada K-Means, klasifikasi dinamis pada FCMmenunjukkan perbaikan pada nilai intra dan inter dimana nilai-nilai tersebut menunjukkan kedekatan antarelemen tiap kluster dan seberapa jauh jarak pisah antar pusat-pusat kluster. Kata Kunci : Fuzzy C-Means Clustering (FCM), Kualitas Kluster, Klasifikasi Dinamis
Strategy Planning for Rice Seed Producer’s Information System Using Anita Cassidy Method Jaja, Jaja; Purwanti, Santi; Herlawati, Herlawati
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 8 No. 2 (2020): September 2020
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v8i2.2277

Abstract

Strategic planning of information systems on seed producers is an action that needs to be taken in order to maintain and make the business grow faster. However, such information system usually been created through the system development life cycle (SDLC) or other methods that appropriate for business. This study used Anita Cassidy method for strategic planning of rice seed producer’s information systems in XYZ Ltd., as case study. The following stages has been done, i.e. visioning, analysis, direction, and recommendations. The final results of the stages carried out recommend business applications that can be implemented, namely: Rice seed certification information systems, E-Commerce of Rice Seeds, Rice Type Determination Applications, Irrigation Control Applications, Paddy Pest Resolution Applications, Rice Growth Monitoring Applications, Personnel Information Systems, Payroll Information System, Procurement Information System, Financial Information System. Keywords: Information Systems, Information Systems Strategic Planning, Anita Cassidy Method
Identification of Website-Based Product Sales Frequency Patterns using Apriori Algorithms and Eclat Algorithms at Rio Food in Bekasi Pramuhesti, Salwa Nabiila; Herlawati, Herlawati; Lestari, Tyastuti Sri
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 11 No. 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5941

Abstract

Sales reports that are not managed automatically may hinder businesses from accurately determining their progress in the short or long term. With increasing community needs for a product, business owners have an opportunity to market their products to a larger audience. The abundance of data highlights the need for information to produce patterns that can be used as a reference for making decisions in buying products on the website. Data mining algorithms can provide support for analysis, which can help avoid inaccurate business progress reports. In this study, the Apriori and Eclat algorithms were applied to analyze frequent itemsets in association rule mining. The dataset used in this study consists of 20 transaction data from frozen food sales. The results showed that the combination of Nugget and Chicken Sausage itemsets were the most frequent, with higher support, confidence, and lift ratio values than the others. These results can be used as product recommendations that are most in demand by customers.
Sentiment Analysis of On-Demand Ride-Hailing Systems using Support Vector Machine and Naïve Bayes Wiguna, Bhagaskara Farhan; Herlawati, Herlawati; Yusuf, Ajif Yunizar Pratama
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 11 No. 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7384

Abstract

Gojek is one of Indonesia's most popular online transportation, founded in 2010. The Gojek application has been downloaded one hundred forty-two million times with more than two million drivers and four hundred thousand partners in food delivery services. Due to the increasing use of the Gojek application and the importance of knowing user views about the services provided by the application. In this research, the sentiment analysis is using Support Vector Machine and the Naïve Bayes method to classify positive sentiment and negative sentiment. The target label focus on positive and negative labels to aims avoid the bias that exists in neutrally labeled reviews on the Gojek Application. The research process includes data collection, pre-processing the data, weighting with Term Frequency-Invers Document Frequency, Support Vector Machine, and Naïve Bayes training by dividing the data into 90% training data and 10% testing data and then evaluating the results using a confusion matrix. The results of testing using the Support Vector Machine algorithm resulted in 90% accuracy, 94% recall, 91% precision, and 94% f1-score, therefore the Naïve Bayes algorithm produces 77% accuracy, 96% recall, 77% precision, and 85% f1-score.

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