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All Journal Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURTEKSI International Journal of Advances in Data and Information Systems Bulletin of Computer Science and Electrical Engineering (BCSEE) eProceedings of Engineering Warta LPM

Edi Sutoyo

Universitas Telkom

Author-ID : 2458235

Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

Published : 12 Documents Claim Missing Document

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Penerapan SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Television Advertisement Performance Rating Menggunakan Artificial Neural Network Sutoyo, Edi; Fadlurrahman, M Asri
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 6, No 3 (2020): Volume 6 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v6i3.42896

Dalam data nyata, ada banyak situasi di mana jumlah instance di satu class jauh lebih sedikit daripada jumlah instance di class lain. Keadaan ini disebut sebagai masalah dataset tidak seimbang (imbalance class). Imbasnya kinerja klasifikasi biasanya menurun di beberapa aplikasi data mining. Pada penelitian ini, diidentifkasi bahwa dataset performansi rating iklan TV yang digunakan memiliki permasalahan imbalance class yang sangat besar dimana instance yang memiliki nilai rating tinggi, jauh lebih sedikit dibandingkan instance yang memiliki nilai rating kecil dan menengah. Sehingga diperlukan metode over-sampling untuk mengatasi permasalahan imbalance class tersebut. Metode yang dapat digunakan adalah Synthetic Minority Over-sampling Technique (SMOTE). Untuk memvalidasi keefektifan model yang diusulkan, dilakukan dua skenario eksperimental yaitu: pertama algoritma ANN langsung digunakan untuk pemodelan tanpa mempertimbangkan ketidakseimbangan kelas, dan kedua dilakukan over-sampling SMOTE untuk meningkatkan jumlah dataset agar mencapai dataset yang seimbang. Hasil eksperimen menunjukkan bahwa performansi ANN+SMOTE mencapai akurasi sebesar 87.06% dibandingkan ANN yang hanya sebesar 86.35%. Penerapan Teknik SMOTE terbukti dapat mengatasi masalah ketidakseimbangan data dan mendapatkan hasil klasifikasi yang lebih baik.

A Soft Set-based Co-occurrence for Clustering Web User Transactions Edi Sutoyo; Iwan Tri Riyadi Yanto; Rd Rohmat Saedudin; Tutut Herawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i3.6382

Grouping web transactions into some clusters are essential to gain a better understanding the behavior of the users, which e-commerce companies widely use this grouping process. Therefore, clustering web transaction is important even though it is challenging data mining issue. The problems arise because there is uncertainty when forming clusters. Clustering web user transaction has used the rough set theory for managing uncertainty in the clustering process. However, it suffers from high computational complexity and low cluster purity. In this study, we propose a soft set-based co-occurrence for clustering web user transactions. Unlike rough set approach that uses similarity approach, the novelty of this approach uses a co-occurrence approach of soft set theory. We compare the proposed approach and rough set approaches regarding computational complexity and cluster purity. The result demonstrates better performance and is more effective so that lower computational complexity is achieved with the improvement more than 100% and cluster purity is higher as compared to two previous rough set-based approaches.

Twitter sentiment analysis of the relocation of Indonesia's capital city Edi Sutoyo; Ahmad Almaarif
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media.

Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province Nabila Amalia Khairani; Edi Sutoyo
International Journal of Advances in Data and Information Systems Vol. 1 No. 1 (2020): April 2020 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/ijadis.v1i1.13

Forest and land fires are disasters that often occur in Indonesia. In 2007, 2012 and 2015 forest fires that occurred in Sumatra and Kalimantan attracted global attention because they brought smog pollution to neighboring countries. One of the regions that has the highest fire hotspots is West Kalimantan Province. Forest and land fires have an impact on health, especially on the communities around the scene, as well as on the economic and social aspects. This must be overcome, one of them is by knowing the location of the area of ??fire and can analyze the causes of forest and land fires. With the impact caused by forest and land fires, the purpose of this study is to apply the clustering method using the k-means algorithm to be able to determine the hotspot prone areas in West Kalimantan Province. And evaluate the results of the cluster that has been obtained from the clustering method using the k-means algorithm. Data mining is a suitable method to be able to find out information on hotspot areas. The data mining method used is clustering because this method can process hotspot data into information that can inform areas prone to hotspots. This clustering uses k-means algorithm which is grouping data based on similar characteristics. The hotspots data obtained are grouped into 3 clusters with the results obtained for cluster 0 as many as 284 hotspots including hazardous areas, 215 hotspots including non-prone areas and 129 points that belong to very vulnerable areas. Then the clustering results were evaluated using the Davies-Bouldin Index (DBI) method with a value of 3.112 which indicates that the clustering results of 3 clusters were not optimal.

Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes Classifier Edi Sutoyo; Ahmad Almaarif
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The quality of students can be seen from the academic achievements, which are evidence of the efforts made by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria that are stated by fulfilling the requirements to continue his studies, the student may have the potential to not graduate on time or even Drop Out (DO). The high number of students who do not graduate on time or DO in higher education institutions can be minimized by detecting students who are at risk in the early stages of education and is supported by making policies that can direct students to complete their education. Also, if the time for completion of student studies can be predicted then the handling of students will be more effective. One technique for making predictions that can be used is data mining techniques. Therefore, in this study, the Naive Bayes Classifier (NBC) algorithm will be used to predict student graduation at Telkom University. The dataset was obtained from the Information Systems Directorate (SISFO), Telkom University which contained 4000 instance data. The results of this study prove that NBC was successfully implemented to predict student graduation. Prediction of the graduation of these students is able to produce an accuracy of 73,725%, precision 0.742, recall 0.736 and F-measure of 0.735.

Analysis of Citizens Acceptance for e-Government Services in Bandung, Indonesia: The Use of the Unified Theory of Acceptance and Use of Technology (UTAUT) Model Kahfi Ahadian Mutaqin; Edi Sutoyo
Bulletin of Computer Science and Electrical Engineering Vol. 1 No. 1 (2020): June 2020 - Bulletin of Computer Science and Electrical Engineering
Publisher : Indonesian Scientific Journal

At present, the use of ICT is growing very rapidly. This has led to changes in processes, functions, and policies in various sectors, including the public service sector managed by the government. e-Government is a new mechanism between the government and the community and stakeholders, which involves the implementation of information technology, and aims to improve the quality (quality) of public services. Bandung City is one of the cities that is very intensive in developing the use of ICT in implementing e-Government. The focus of the city government of Bandung is the Government to Citizen (G2C) application model. The application that is still lacking in use is the e-punten application. One important factor for the success of e-Government services is the acceptance and willingness of people to use e-Government services. E-punten application services provided by the Bandung city government will not run perfectly if no people are using it. To assess what factors influence the use of e-punten applications in the city of Bandung, the UTAUT model is used. To analyze the factors that influence the acceptance of e-punten applications SEM analysis is used. In this study, the PLS-SEM approach is used to solve multiple regression when specific problems occur in the data, such as the small sample size of the study. The PLS evaluation is carried out by evaluating the measurement model and the structural model that best suits the UTAUT model. Factors that influence the use of e-punten applications are effort expectancy for behavioral intention, facilitating conditions for use behavior, and behavioral intention for use behavior. The factors that have the most influence are performance expectancy and effort expectancy.

WeCare Project: Development of Web-based Platform for Online Psychological Consultation using Scrum Framework Frisca Febriyani Kurniawan; Fauzan Radifan Shidiq; Edi Sutoyo
Bulletin of Computer Science and Electrical Engineering Vol. 1 No. 1 (2020): June 2020 - Bulletin of Computer Science and Electrical Engineering
Publisher : Indonesian Scientific Journal

Symptoms and signs of mental disorders depend on the type of disorder experienced. Patients can experience interference with emotions, thought patterns, and behavior. Ways to determine the diagnosis and check whether you have a mental illness, there are several ways in the form of physical examination through a doctor, laboratory tests, psychological evaluation. There is also therapy as a healing process and recovery of the soul that is healthy. In Indonesia, many people experience mental health problems, but most are not aware of and care about these problems. Even in the worst cases, many cases lead to suicide. One factor that triggers suicide is, people who experience mental health problems do not have a place to talk about the problems they are facing, because in each city itself not all the availability of psychologist services that can help sufferers of mental health problems, let alone remember the price of psychologist services offered is quite expensive. Therefore, by taking into account these considerations, the WeCare project proposes developing a web-based platform for online psychologist consultation. In general, this platform has three main actors namely, admin, user, and psychologist. This platform is expected to be a media liaison between users (patients) with psychologists in conducting consultations.

PERBANDINGAN ALGORITMA KLASIFIKASI SUPPORT VECTOR MACHINE DAN NAIVE BAYES PADA IMBALANCE DATA Chika Enggar Puspita; Oktariani Nurul Pratiwi; Edi Sutoyo
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 8, No 1 (2021): Desember 2021
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v8i1.1185

Abstract: Question classification is a computer science system, which aims to analyze questions and can label each question based on existing categories. Questions can be collected from several materials or topics that are many and different. Therefore, the researcher intends to create a classification system for quiz questions Data Warehouse and Business Intelligence which can be grouped into topics Data Warehouse, Business Intelligence, Data Analytics, and Performance Measurement. One way to solve this problem is by approach machine learning. In this study, researchers used a comparison of machine learning algorithms, namely the algorithm NaïveBayes and SupportVectorMachine using SMOTE and methods Cross-Validation The results of this study show the best accuracy results and are very helpful. The results obtained in the method cross-validation before SMOTE resulted in an accuracy rate of 82.02% for the results after going through the SMOTE stage of 94.79% on the algorithm Naïve Bayes, while the algorithm SupportVectorMachine get accuracy of 81.39% in the process before SMOTE for the results after going through SMOTE of 96.52%. Keywords: Cross-Validation; Machine Learning; Naive Bayes; Support Vector Machine; Question Classification Abstrak: Klasifikasi pertanyaan merupakan sebuah sistem ilmu komputer, yang bertujuan untuk menganalisis pertanyaan serta dapat memberi label pada setiap pertanyaan berdasarkan kategori yang ada. Pertanyaan soal dapat dikumpulkan dari beberapa materi atau topik yang banyak dan berbeda. Oleh karena itu, bermaksud untuk membuat sistem klasifikasi pertanyaan soal kuis Data Warehouse dan Business Intelligence yang dapat dikelompokkan menjadi topik Data Warehouse, Business Intelligence, Data Analitik, dan Pengukuran Kinerja. Cara yang dapat dilakukan untuk permasalahan ini dengan menggunakan pendekatan MachineLearning. Pada penelitian kali ini menggunakan perbandingan algoritma MachineLearning yaitu algoritma NaïveBayes dan SupportVectorMachine menggunakan metode SMOTE dan Cross-Validation. Hasil penelitian ini menunjukkan hasil akurasi yang terbaik dan sangat membantu. Hasil yang diperoleh pada metode cross-validation sebelum SMOTE menghasilkan tingkat akurasi sebesar 82.02% untuk hasil sesudah melalui tahap SMOTE sebesar 94.79 % pada algoritma Naïve Bayes, sedangkan pada algoritma Support Vector Machine menghasilkan akurasi sebesar pada proses sebelum SMOTE 81.39% untuk hasil sesudah melalui SMOTE sebesar 96.52%. Kata kunci: Klasifikasi Pertanyaan; Pembelajaran Mesin; Naive Bayes; Support Vector Machine; Cross-Validation

Perancangan Business Intelligence Dashboard Untuk Mendukung Keputusan Dalam Penyediaan Layanan Jaringan Berdasarkan Traffic Jaringan Internet Telkomsel Menggunakan Metode Business Dimensional Lifecycle Revo Faris Saifuddin; Rachmadita Andreswari; Edi Sutoyo
eProceedings of Engineering Vol 8, No 4 (2021): Agustus 2021
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Operator telekomunikasi seluler (Telkomsel) dengan berbagai produk dan layanan dalam memenuhi kebutuhan pengguna operator seluler. Layanan yang ditawarkan tidak hanya layanan suara, multimedia, dan internet. Berdasarkan APJII jumlah pengguna internet pada periode tahun 2019 – 2020 di Indonesia mencapai 196,7 juta, meningkat dari tahun 2018 dengan jumlah 23,5 juta atau 8,9 persen. Kepala Bidang Yayasan Lembaga Konsumen Indonesia (YLKI) mencatat gangguan Jaringan internet banyak dikeluhkan saat pandemi dengan pengaduan terkait permasalahan telekomunikasi pada tahun 2020, persentase pengaduan terhadap operator tertinggi yaitu Telkomsel sebesar 29,7 persen. Jumlah pengguna layanan jaringan internet yang sangat banyak dapat menurunkan nilai quality of service dan meningkatkan packet loss pengguna jaringan. Implementasi business intelligence menggunakan pentaho dengan metode business dimensional life cycle pada PT. Telkomsel. Pengolahan data pada penelitian ini menggunakan proses ETL (extraction, transform, and load) dengan menggunakan Pentaho Data Integration. Hasil dari penelitian tugas akhir ini yaitu tampilan business intelligence dashboard, berupa pengguna layanan teknologi internet berdasarkan perangkat yang digunakan untuk mengakses layanan internet dan pengguna layanan teknologi jaringan internet berdasarkan teknologi jaringan internet pada wilayah regional Jabotabek. Analisis yang dihasilkan bertujuan untuk pertimbangan bagi pihak PT. Telkomsel dalam meningkatkan layanan jaringan pada wilayah pengguna yang tinggi sehingga dapat meningkatkan customer retention pada perusahaan Telkomsel. Kata Kunci: Business Intelligence, ETL, Dashboard

Analisis Data Mining Untuk Klasifikasi Data Kualitas Udara Dki Jakarta Menggunakan Algoritma Decision Tree Dan Support Vector Machine Adinda Inez Sang; Edi Sutoyo; Irfan Darmawan
eProceedings of Engineering Vol 8, No 5 (2021): Oktober 2021
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Pertumbuhan dan perkembangan suatu kota merupakan salah satu faktor penyebab terjadinya pencemaran udara karena kualitas udara sudah tergabung dengan berbagai komponen senyawa. Menurut IQ Air 2020 prov. Untuk memantau pencemaran udara setiap harinya, Dinas Lingkungan Hidup Pemerintah Provinsi DKI Jakarta mengoperasikan Stasiun Pemantau Kualitas Udara (SPKU). Penggunaan data mining merupakan metode yang cocok untuk mengetahui informasi pencemaran udara di Provinsi DKI Jakarta. Metode data mining yang digunakan yaitu klasifikasi karena metode ini dapat mengolah data parameter ISPU menjadi informasi yang memberitahukan tingkat kualitas udara perharinya dengan menggunakan algoritma Decision Tree dan Support Vector Machine (SVM). Hasil dari penerapan data mining untuk klasifikasi kualitas udara di DKI Jakarta yaitu algoritma Decision Tree memiliki performa yang lebih baik dengan rasio terbaik 90:10 dibandingkan dengan algoritma SVM dengan rasio terbaik 60:40 dan untuk melakukan klasifikasi kualitas udara di DKI Jakarta. Pada algoritma Decision Tree mendapatkan nilai Precision sebesar 99,02%, Recall 99,73%, F1-Measure 99,37%, Akurasi 99,40% dan pada algoritma SVM mendapatkan nilai Precision sebesar 95,82%, Recall 88,89%, F1-Measure 92,22% dan Akurasi 94,93%. Kata kunci : Klasifikasi, Kualitas Udara, Decision Tree, SVM

Co-Authors Adinda Inez Sang Chika Enggar Puspita Fadlurrahman, M Asri Fauzan Radifan Shidiq Frisca Febriyani Kurniawan Gilang Erlangga Hanif Fakhrurroja Iqbal Santosa Irfan Darmawan Iwan Tri Riyadi Yanto Kahfi Ahadian Mutaqin Nabila Amalia Khairani Oktariani Nurul Pratiwi Rachmadita Andreswari Rahmat Fauzi Rd. Rohmat Saedudin Revo Faris Saifuddin Tutut Herawan

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