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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) OPSI JUITA : Jurnal Informatika Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Briliant: Jurnal Riset dan Konseptual Journal of Development Research Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal Jurnal INSYPRO (Information System and Processing) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Industri Terintegrasi (JUTIN) Community Empowerment Jurnal Teknologi Informasi Cyberku Jurnal Algoritma Jurnal Riptek
Heru Agus Santoso
Universitas Dian Nuswantoro
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Transportation

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OPTIMASI K-MEANS CLUSTERING UNTUK IDENTIFIKASI DAERAH ENDEMIK PENYAKIT MENULAR DENGAN ALGORITMA PARTICLE SWARM OPTIMIZATION DI KOTA SEMARANG Rustam, Suhardi; Santoso, Heru Agus; Supriyanto, Catur
ILKOM Jurnal Ilmiah Vol 10, No 3 (2018)
Publisher : Program Studi Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (585.534 KB) | DOI: 10.33096/ilkom.v10i3.342.251-259

Abstract

Tropical regions is a region endemic to various infectious diseases. At the same time an area of high potential for the presence of infectious diseases. Infectious diseases still a major public health problem in Indonesia. Identification of endemic areas of infectious diseases is an important issue in the field of health, the average level of patients with physical disabilities and death are sourced from infectious diseases. Data Mining in its development into one of the main trends in the processing of the data. Data Mining could effectively identify the endemic regions of hubunngan between variables. K-means algorithm klustering used to classify the endemic areas so that the identification of endemic infectious diseases can be achieved with the level of validation that the maximum in the clustering. The use of optimization to identify the endemic areas of infectious diseases combines k-means clustering algorithm with optimization particle swarm optimization ( PSO ). the results of the experiment are endemic to the k-means algorithm with iteration =10, the K-Fold =2 has Index davies bauldin = 0.169 and k-means algorithm with PSO, iteration = 10, the K-Fold = 5, index davies bouldin = 0.113. k-fold = 5 has better performance.
Fake Twitter Account Classification of Fake News Spreading Using Naïve Bayes Santoso, Heru Agus; Rachmawanto, Eko Hari; Hidayati, Ulfa
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.25747

Abstract

Twitter is a very popular microblog, where users can search for various information, current news, celebrity posts, and hot topics. Indonesia is ranked 5th for the most Twitter users. The large number of users makes Twitter used for the benefit of certain parties with bad goals, such as spreading fake news using fake accounts. Fake accounts are often used by several parties to spread fake news, therefore the spread of fake news must be immediately limited to minimize the negative impact caused by fake news. For this reason, this research is written with the aim of being able to classify fake and genuine Twitter accounts. In this study, using data mining techniques that are closely related to big data in decision making by applying the Naive Bayes method. Naïve Bayes is one of the most widely used classification methods because it has good accuracy and faster computation time. The classification process uses nine parameters, namely based on the Profile Created, Favorite Count, Follower Count, Following Count, Geo Enabled, Follower Rate, Following Rate, Follower Following Ratio, Verified. This study uses 210 datasets of twitter accounts that spread fake news, the result is that Naïve Bayes works very promising  in the classification of fake twitter accounts and in the testing process using 5% of training set produces an accuracy of 80%.
PENINGKATAN ALGORITMA NAIVE BAYES PADA KLASIFIKASI KATEGORI REVIEW PELANGGAN TERHADAP PRODUK DENGAN CORRELATION-BASED FEATURE SUBSET SELECTION (CSF) Rizaldy, Adhy; Santi, Dian Nirmala; Santoso, Heru Agus
Jurnal INSYPRO (Information System and Processing) Vol 5 No 2 (2020)
Publisher : Prodi Sistem Informasi UIN Alauddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (26.365 KB) | DOI: 10.24252/insypro.v5i2.18662

Abstract

Loyalitas pelanggan, keputusan membeli kembali serta kepuasan merupakan salah satu konsep yang paling sering diteliti oleh akademisi. Kesetiaan, pembelian kembali dan kepuasan pelanggan memiliki dampak yang kuat terhadap kinerja perusahaan dengan memberikan keunggulan kompetitif, semakin banyak pelanggan setia, maka tingkat kepuasan pelanggan meningkat. Meskipun sebenarnya penelitian (hubungan antara loyalitas pelanggan, pembelian kembali dan kepuasan) ini dilakukan secara ektensif, namun semuanya tampak kompleks dan multidimensional, sehingga sulit dipahami dengan baik.Pada penentuan tipe pelanggan berdasarkan review komentar online, fitur yang dihasilkan dari dokumen review tersebut sangat banyak. Sehingga akurasi klasifikasi rendah.Adapun beberapa teknik seleksi fitur dengan metode filter yaitu dokumen frequency dan ChiSquare. Metode Chi Square lebih sering digunakan untuk pengklasifikasian dengan dokumen yang banyak bila dibandingkan Information Gain. Semakin banyak jumlah dokumen yang digunakan dalam klasifikasi dapat meningkatkan nilai F-Measure klasifikasi teks dengan metode Chi Square.Berdasarkan analisa di atas maka penulis akan menggunakan metode Naive Bayes dengan Correlation-Based Feature Subset Selection (CFS). Dengan metode tersebut, diharapkan akan menghasilkan klasifikasi pelanggan dengan nilai akurasi yang baik untuk digunakan dalam review produk.Dengan menggunakan Cfs mengalami peningkatan nilai recall dari 88,50%, menjadi 94,78%. Dengan demikian membuktikan bahwa algoritma ini mampu menjawab NB cukup optimal digunakan untuk mengklasifikasikan pelanggan pada dataset komentar toko online
Fake Twitter Account Classification of Fake News Spreading Using Naïve Bayes Santoso, Heru Agus; Rachmawanto, Eko Hari; Hidayati, Ulfa
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.25747

Abstract

Twitter is a very popular microblog, where users can search for various information, current news, celebrity posts, and hot topics. Indonesia is ranked 5th for the most Twitter users. The large number of users makes Twitter used for the benefit of certain parties with bad goals, such as spreading fake news using fake accounts. Fake accounts are often used by several parties to spread fake news, therefore the spread of fake news must be immediately limited to minimize the negative impact caused by fake news. For this reason, this research is written with the aim of being able to classify fake and genuine Twitter accounts. In this study, using data mining techniques that are closely related to big data in decision making by applying the Naive Bayes method. Naïve Bayes is one of the most widely used classification methods because it has good accuracy and faster computation time. The classification process uses nine parameters, namely based on the Profile Created, Favorite Count, Follower Count, Following Count, Geo Enabled, Follower Rate, Following Rate, Follower Following Ratio, Verified. This study uses 210 datasets of twitter accounts that spread fake news, the result is that Naïve Bayes works very promising  in the classification of fake twitter accounts and in the testing process using 5% of training set produces an accuracy of 80%.
APLIKASI BERBAKTI: PERANCANGAN ARSITEKTUR PERANGKAT LUNAK PENGHUBUNG ANAK DENGAN ORANG TUA Heru Agus Santoso; Fahri Firdausillah; Septian Enggar Sukmana; Tjetjep Witjahjono; Arkav Juliandri
Techno.Com Vol 15, No 3 (2016): Agustus 2016
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (529.794 KB) | DOI: 10.33633/tc.v15i3.1239

Abstract

Kebutuhan perawatan kondisi orang tua sangat perlu dilakukan seiring dengan berjalannya waktu. Hambatan mulai terjadi jika antara orang tua dan anak terpisah dalam kondisi yang cukup jauh sehingga anak sebagai pihak perawat orang tua tidak dapat maksimal dalam memberikan pemantauan dan perawatan secara intens, terlebih lagi perkembangan teknologi membuat orang tua pun kesulitan dalam menggunakan teknologi terkini. Untuk mengakomodasi dan memberikan solusi atas permasalahan tersebut maka perlu pengembangan perangkat lunak yang memudahkan anak dalam memantau dan merawat orang tua secara intens. Perangkat lunak yang dikembangkan terdiri dari dua buah perangkat lunak yang saling terhubung yaitu caregiver app yang digunakan oleh anak dan elder app yang digunakan oleh orang tua. Oleh karena dalam hal ini orang tua juga menggunakan perangkat lunak aplikasi tersebut, maka elder app dikembangkan secara khusus supaya mudh digunakan oleh orang tua. Tidak hanya pada elder app, desain sistem pada perangkat lunak ini dimodelkan secara khusus dan menjadi pembahasan dalam paper ini. Kata Kunci: caregiving, orangtua, anak, perkuat jalinan hubungan
Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization Heru Agus Santoso; Eko Hari Rachmawanto; Adhitya Nugraha; Akbar Aji Nugroho; De Rosal Ignatius Moses Setiadi; Ruri Suko Basuki
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14744

Abstract

Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis, the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.
Model Fuzzy Tsukamoto untuk Klasifikasi dalam Prediksi Krisis Energi di Indonesia Achmad Zaki; Heru Agus Santoso
Creative Information Technology Journal Vol 3, No 3 (2016): Mei - Juli
Publisher : UNIVERSITAS AMIKOM YOGYAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (846.548 KB) | DOI: 10.24076/citec.2016v3i3.76

Abstract

Krisis energi dunia juga terjadi di Indonesia. Cadangan energi di Indonesia terutama energi fosil (minyak bumi, batubara, dan gas alam) semakin hari semakin menyusut. Ketersediaan akan energi fosil juga semakin berkurang karena peningkatan konsumsi energi per kapita. Untuk memprediksi krisis energi di Indonesia, paper ini mengusulkan pengembangan sistem inferensi fuzzy sukamoto untuk klasifikasi krisis energi berdasarkan parameter jumlah produksi, konsumsi energi dan faktor penggerak kebutuhan energi, yakni GDP dan populasi penduduk. Luaran dari sistem ini adalah klasifikasi berdasarkan parameter tersebut, yaitu kondisi aman, waspada dan krisis. Hasil eksperimen menunjukan sistem yang dibangun menghasilkan tingkat akurasi pada minyak bumi 90%, batubara 100 % dan gas alam 100%. Dengan adanya sistem ini diharapkan mampu memberikan peringatan dini dan pendukung keputusan bagi pemerintah atau pihak instansi terkait dalam memberikan penangan atau solusi terhadap masalah krisis energi. World energy crisis also occurred in Indonesia. Energy reserves in Indonesia, especially fossil fuels (petroleum, coal, and natural gas) are increasingly shrinking. The availability of fossil energy will also be on the wane because of an increase in energy consumption per capita. To predict the energy crisis in Indonesia, this paper proposes the development of sukamoto fuzzy inference systems for classification energy crisis based on parameters the amount of production, energy consumption and energy demand driven factors, namely GDP and population. Outcomes of this system is the classification based on these parameters, i.e., a safe condition, alert and crisis. The experimental results show the system produce levels of accuracy at 90% petroleum, natural gas 100% and CoA, 100%. This system are expected to provide an early warning and decision support for the government or the relevant agencies in giving the handlers or the solution to the problem of energy crisis. 
Development of Bot for Microservices Server Monitoring Using Life Cycle Approach to Network Design Method Setyadhi Putra Deriyanto; Heru Agus Santoso
JUITA : Jurnal Informatika JUITA Vol. 8 Nomor 2, November 2020
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1041.768 KB) | DOI: 10.30595/juita.v8i2.8422

Abstract

This study was conducted on the emergency response system that has been implemented in Demak, Kendal and Batang districts, namely Tanggapin. This system provides services in the form of reporting and actions, as well as information about the health sector, from the availability of hospital rooms to the availability of blood at PMI. Tanggapin uses a mobile-based and web-based platform, where the web is used as a medium for operators to input data and validate incoming reports. Due to complex system services with various supporting features, it requires a microservices architecture to operate independently and in a distributed manner. Because of this dynamic system, realtime monitoring support for server performance is needed via Telegram Bot. The monitoring system was built using the PPDIOO Lifecycle Approach to Network Design and Implementation method because it is reliable and the flow of a process never breaks. The format used to exchange data uses the JSON (Javascript Object Notation) format. This study shows that the monitoring system run well because every test carried out gives the expected results, namely the admin receives a warning message via the Telegram Bot.
Naive Bayes Classification pada Klasifikasi Dokumen Untuk Identifikasi Konten E-Government Akhmad Pandhu Wijaya; Heru Agus Santoso
Journal of Applied Intelligent System Vol 1, No 1 (2016): Februari 2016
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v1i1.1032

Abstract

Kebutuhan informasi adalah aspek penting yang harus dipertimbangkan, tidak semua informasi adalah informasi yang dibutuhkan. Karena banyaknya informasi digital dalam bahasa Indonesia, perlu untuk clustering dokumen berdasarkan apa yang dicari sehingga untuk mendapatkan beberapa informasi dapat dilakukan dengan sesuai, ringkas, menyeluruh, dan sesuai kebutuhan. Banyak penelitian tentang klasifikasi dokumen telah dibuat dan dikembangkan untuk mendapatkan hasil yang baik, penelitian pada beberapa website yang memiliki sumber informasi skala besar dan membutuhkan klasifikasi untuk mendapatkan informasi yang berkualitas dari situs yang diperiksa. Klasifikasi ini teknik dari data mining dan pertambangan teks juga digunakan untuk mencari atau mengatur kelas dibedakan dengan menggunakan beberapa fungsi dengan tujuan memungkinkan model untuk digunakan untuk data pengujian. Pada  penelitian ini, objeknya adalah Situs Web Jawa Tengah dan diklasifikasikan oleh Naïve Bayes Classification (NBC). Dengan menggunakan metode ini diharapkan memfasilitasi klasifikasi dokumen bahasa Indonesia untuk identifikasi konten e-government. Kata kunci— klasifikasi, dokumen, Naïve Bayes, e-government.
RETRACTED: Klasifikasi Data Time Series Arus Lalu Lintas Jangka Pendek Menggunakan Algoritma Adaboost dengan Random Forest Ahmad Rofiqul Muslikh; Heru Agus Santoso; Aris Marjuni
BRILIANT: Jurnal Riset dan Konseptual Vol 4, No 1 (2019): Volume 4 Nomor 1, Februari 2019
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (302.293 KB) | DOI: 10.28926/briliant.v4i1.272

Abstract

RETRACTEDFollowing a rigorous, carefully concerns and considered review of the article published in BRILIANT: Jurnal Riset dan Konseptual to article entitled Klasifikasi Data Time Series Arus Lalu Lintas Jangka Pendek Menggunakan Algoritma Adaboost Dengan Random Forest Vol 4, No 1, pp. 78-96, February 2019, DOI: http://dx.doi.org/10.28926/briliant.v3i3.272.This article has been found to be in violation of the BRILIANT: Jurnal Riset dan Konseptual Publication principles and has been retracted.The editor investigated and found that the article published in Jurnal Teknologi Informasi CyberKU Vol. 14 no 1 January 2018, pp. 24-38.The document and its content has been removed from BRILIANT: Jurnal Riset dan Konseptual, and reasonable effort should be made to remove all references to this article.
Co-Authors Achmad Naila Muna Ramadhani Achmad Zaki Adhe Irham Thoriq Adhitya Nugraha Adhitya Nugraha Akbar Aji Nugroho Akhmad Pandhu Wijaya, Akhmad Pandhu Alfani, Wahyu Amiril Mukminin Arga Dwi Pambudi Arkav Juliandri Arkav Juliandri Assiva, Mochamad Amry Atanasius Marcello Aziz, Halim Al Catur Supriyanto Catur Supriyanto Catur Supriyanto De Rosal Ignatius Moses Setiadi Dewa Kusuma Wijaya Eko Hari Rachmawanto Fahri Firdausillah Farah Zakiyah Rahmanti Herwin Suprijono Heryanto, M Ary Hidayati, Ulfa Jeffry, Muhammad Kusmiyati Kusmiyati Luh Putu Ratna Sundari Muchti, Muhammad Agusdika Ridho Muhamad Haris Zuhri Muslikh, Ahmad Rofiqul Nur Islahudin Pujiono Pujiono Purwanto Purwanto Rahadian, Helmy Rama Tri Agung Ratih Setyaningrum Rifki, Ahmad Muhson Rizaldy, Adhy Roiya Ravida Ruri Suko Basuki Rustam, Suhardi Santi, Dian Nirmala Saraswati, Galuh Wilujeng Septian Enggar Sukmana Setiadi, Kristoforus Adrian Setyadhi Putra Deriyanto Suhardi Rustam Tamami, Aries Jehan Tjetjep Witjahjono Wijaya, Dewa Kusuma Wijaya, S.T, M.Sc, A.MP, Dewa Kusuma Wiradinata, Halim Jayakusuma Yanimaharta, Arya Zaenal Arifin