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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Disability Studies Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Budi Darma Setiawan
Fakultas Ilmu Komputer , Universitas Brawijaya
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mechanical Engineering Social Sciences

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Implementasi High Order Fuzzy Time Series Multifactor pada Prediksi Harga Ayam Broiler di Pasar Malang Fanny Aulia Dewi; Budi Darma Setiawan; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Malang is the second largest population after Surabaya in East Java Province. Malang, which has increased its population every year, has resulted in the need for staples, especially broiler chicken meat, to increase. Broiler chicken meat is one of the sources of nutritious animal protein. Broiler chicken meat can be consumed by all levels of society so that there is an increase in demand every year. The availability of broiler chicken meat must always be fulfilled in the market and must pay attention to the price too. Broiler chicken meat prices on religious holidays (such as Eid al-Fitr, Eid Al-Adha, Christmas) there is a very striking increase compared to prices on normal days. The traders who need information about the price of broiler chickens every day in order to arrange sales so as not to miss. Therefore, a settlement is needed in forecasting the price of chicken using 72 data obtained from BPS in the period January 2013-December 2018. Based on the results of this study, the best MSE is 1,430 based on the highest number of orders.
Klasifikasi Berat Badan Lahir Rendah Pada Bayi Dengan Fuzzy K-Nearest Neighbor Muhammad Rizkan Arif; Budi Darma Setiawan; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The number of infant mortality (IMR) is a measure of the success of health services in an area. The lower the IMR, the better the health services in the area. However, in 2015, the IMR value in Indonesia was very far from the agreed target as an indicator of the success of health service development. In 2013, there was an increase in LBW cases during the 2009-2013 period to 16% according to data from WHO and UNICEF. If viewed from the cause of death, low birth weight babies still rank high. As many as 2.79% of infants died from LBW in East Java in 2010. This percentage increased to 3.32% in 2013 so that LBW was classified as the main cause of neonatal death, which was 38.03% of the total birth rate. The existence of an early detection system is likely that LBW is expected to be able to help reduce infant mortality. One method that can be applied to predict the possibility of LBW is Fuzzy K-Nearest Neighbor (FK-NN). This method is proven to be able to carry out LBW classification with an accuracy rate of 79%.
Prediksi Permintaan Semen Dengan Metode Fuzzy Time Series Yosendra Evriyantino; Budi Darma Setiawan; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cement is a material that is used as an adhesive for solid materials namely bricks or concrete blocks into a strong and sturdy unit, usually used for making houses, walls, foundations, roads, or other buildings. In Indonesia, cement production is quite high. In 2017, the total cement production in Indonesia has reached 107.9 million tons. However, high cement production in Indonesia is not matched by the number of requests. As a result, Indonesia experienced an oversupply of cement which caused the price of cement in the market to experience a decline. Therefore, research on predicting cement demand needs to be done as a solution for cement producers in estimating the amount of cement that needs to be produced. In this study, discussing the Fuzzy Time Series method used to predict the amount of demand for cement. The data used is data collected from PT. Semen Indonesia from 2006 to 2018 for each month. From the test results, the smallest MAPE error value was obtained at 10.42% with a parameter value of 80 intervals for 24 test data and 96 training data.
Pengaruh Seleksi Fitur Information Gain pada K-Nearest Neighbor untuk Klasifikasi Tingkat Kelancaran Pembayaran Kredit Kendaraan Ulfah Mutmainnah; Budi Darma Setiawan; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Intermittent credit is one of the problems or risks that are often faced by some auto loan service providers. The problem stems from the debtor's behavior, namely not paying the installments on time. In determining the smoothness of credit payments depends on the analysis of debtor data, but analyzing for large amounts of data can take up more time. This study uses the Information Gain feature selection and the K-Nearest Neighbor algorithm to overcome the problem of effectiveness and determine the accuracy of the classification level of the smoothness of auto loan payments so as to determine the effect of feature selection. Information Gain feature selection which is used to reduce feature dimensions so that relevant features can be obtained. The selected features are then processed for classification using the K-Nearest Neighbor algorithm. Based on testing from this study, the highest accuracy obtained is 94.44% when testing with a balanced class distribution using the number of features 3 and the value of K = 4 while the lowest accuracy is obtained at 33.33% using the number of features 10 with a value of K = 5 when testing with uneven class distribution. Features that produce the highest accuracy are jobs, income and price on the road (OTR). The three features are features with the largest order of gain values and have a gain value of more than 0.1.
Penerapan Algoritma Support Vector Regression Pada Peramalan Hasil Panen Padi Studi Kasus Kabupaten Malang Dhan Adhillah Mardhika; Budi Darma Setiawan; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rice is one of the important resources in human life, in several surveys it was found that more than 59% of the world's population used rice from rice as food staple. But in another theory stated that the human population will continue to develop exponentially while it is difficult to be followed by the growth of food products, especially in this case rice. Support Vector Regression (SVR) method is a method that will be used in this study, this method has been used in several previous studies such as forecasting gold prices and forecasting electricity consumption. In this study we will focus on testing whether the Support Vector Regression (SVR) method is suitable for use in predicting rice yields, using a number of predetermined parameters, and by applying changes to the parameters, namely the number of iterations, Complexity, Epsilon, Sigma, cLR , Lambda. The best results obtained in this study reached MAPE error rate of 10.133%, these results were achieved with the following parameter values, Number of iterations: 50, Complexity: 1, Epsilon: 0.01, Sigma: 1, cLR: 0.1, Lambda: 1
Prediksi Penjualan Seblak menggunakan Algoritme Extreme Learning Machine di Seblak Malabar Fadhlillah Ikhsan; Budi Darma Setiawan; Tibyani Tibyani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Seblak Malabar is a business in Malang running on food sector. The typical uniqueness of flavor and the diversity of menu which make the food attract many customers. However, because of the impact of some factors, such as weather change and tighter market trend, makes Seblak sale run into the fluctuation. It makes some new problems; those are problem in maximizing the profit and maintaining the stability of logistics. From those problems, the upcoming selling prediction is a solution offered by the researcher because it has an important role to make a decision. The data used for this prediction refers to the previous sale data. That data is time series because it is arranged based on the time. Time series data prediction is very complex problem so that it is needed a method which is able to produce a prediction based on previous data pattern movement. Extreme Learning Machine Algorithm in Artificial Neural Network (ANN) feedforward network is suggested by the researcher because it has very good performance in predicting time series data. From the research conducted, ELM algorithm is able to produce Mean Average Percentage Error (MAPE) up to 1.7548%. MAPE score less than 10% indicates that ELM algorithm can be used to predict the sale of Seblak Malabar.
Klasifikasi Risiko Hipertensi menggunakan Fuzzy K-Nearest Neighbor Deby Chintya; Budi Darma Setiawan; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Hypertension, also known as high blood pressure, is a condition where there's increase of blood pressure above the normal level of 120/80 mmHg. Hypertension can cause cardiovascular disease and increased death risk by coronary heart disease and stroke. According to Riset Kesehatan Dasar in Indonesia, hypertension is the most prevalent health problems with 25,8% percentage in 2013. Development of classification system for hypertension risk can be used to detect early hypertension disease. Classification of hypertension risk in this research uses Fuzzy K-Nearest Neighbor method, with Information Gain feature selection. Accuracy value resulted from the test is 84.0002% with value k=5 using 6 features of blood pressure, fitness, age, fatty foods consumption and caffeine consumption.
Identifying The Influence of Consumer Purchase Intention Through Live Streaming Shopping: A Systematic Literature Review Mindiasari, Irtiyah Izzaty; Priharsari, Diah; Setiawan, Budi Darma; Purnomo, Welly
Journal of Information Technology and Computer Science Vol. 9 No. 1: April 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202491576

Abstract

The rapid development of technology influences some changes in e-commerce. One of them is the emergence of live-streaming shopping, which combines live-streaming technology with e-commerce, social networking, and entertainment. This shopping format allows viewers to interact with the streamer (seller) and instantly make a purchase with just one touch. Consumers who watch live streaming shopping generally are those who initially have an interest in the offered product. According to prior studies, the presence of live shopping can enhance both customer desire to buy and business sales. To investigate the factors influencing purchase intention in live-streaming shopping, a systematic literature review was conducted. A total of 40 factors were found from 13 selected articles containing live-streaming shopping and purchase intention. Based on these factors, 34 had a positive impact, 2 had a negative impact, and 4 had no significant impact on buyer purchase intention.
Phenomenological Investigation of Social Media Technological Aspects Against Cyberbullying from the Third Person Perspective of Higher Education Students Dhewanty, Civica Moehaimin; Priharsari, Diah; Setiawan, Budi Darma
Journal of Information Technology and Computer Science Vol. 9 No. 1: April 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202491578

Abstract

Cyberbullying often occurs among higher education students as they frequently use and easily get access to the internet, especially through social media. However, the awareness of cyberbullying among them is very low. They do not know how to identify cyberbullying and prevent it. This research will discover the characteristics of social media that can help third persons identify and prevent cyberbullying. This research used a qualitative method with a phenomenological approach to study a person’s experience of technology regarding cyberbullying. The data collected in this study were obtained from interviews with higher education students. The data acquired were analyzed using Collaizzi's seven steps descriptive phenomenological analyses. The analysis produced a description of the phenomenon of cyberbullying on social media that have verified by the interviewees. This study found some characteristics of social media to help higher education students identify and prevent cyberbullying and discovers which social media has the most cyberbullying content and the similarities/differences of each social media in helping a third person identify and prevent cyberbullying. And found several technological factors that affect the effort of preventing cyberbullying, such as seeing other people’s activities and personal information on social media, and doing a report or sharing cyberbullying content.
Klasifikasi Emosi pada Komentar YouTube menggunakan Algoritme Support Vector Machine Nashrullah, Nashrullah; Setiawan, Budi Darma
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 9 (2023): September 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Content creator telah menjadi profesi baru yang menjanjikan semenjak pesatnya perkembangan sosial media. Untuk menghasilkan sebuah konten yang dapat dinikmati penonton seorang content creator harus bisa memahami penontonnya. Salah satu cara yang dapat dilakukan adalah dengan mengetahui emosi penonton melalui komentar atau dalam machine learning dikenal juga dengan istilah klasifikasi emosi. Support Vector Machine merupakan algoritme supervised learning yang memiliki keunggulan dalam menggeneralisasi model secara baik dengan memanfaatkan ruang fitur berdimensi tinggi. Penelitian ini menggunakan algoritme Support Vector Machine dan Information Gain sebagai metode seleksi fitur. Dataset yang digunakan adalah komentar YouTube yang telah dilabeli dengan kelas senang, sedih dan marah. Proses klasifikasi emosi ini terdiri dari text preprocessing, seleksi fitur dengan Information Gain, ekstraksi fitur dengan TF-IDF (Term Frequency - Inverse Document Frequency) dan proses klasifikasi menggunakan algoritme Support Vector Machine. Proses pengujian menggunakan metode Stratified K-Fold dengan nilai k = 5. Hasil dari pengujian yang diperoleh adalah sebuah model Support Vector Machine dengan nilai akurasi 88,07% dan f1-measure 88,06%. Pada penelitian ditemukan bahwa penggunaan fitur seleksi Information Gain tidak meningkatkan performa dari model.
Co-Authors Abdul Fatih Achmad Basuki Achmad Fahlevi Addin Sahirah, Rafifa Adinugroho, Sigit Aditya Chandra Nurhakim Aditya Kresna Bayu Arda Putra Agung Nurjaya Megantara Agus Wahyu Widodo Ahmad Afif Supianto Akhmad Eriq Ghozali Akmal Subakti Wicaksana Alfi Nur Rusydi Almira Syawli, Almira Amaliah Gusfadilah Andhi Surya Wicaksana Andika Harlan Angga Dwi Apria Rifandi Anjasari, Ni Luh Made Beathris Aria Bayu Elfajar Asghany, Yusrian Ashidiq, Muhammad Fihan Azmi Makarima Yattaqillah Baihaqi, Galih Restu Barlian Henryranu Prasetio Bayu Rahayudi Bintang, Tulistyana Irfany Budi Santoso Cahyo Adi Prasojo Candra Dewi Candra Dewi Chelsa Farah Virkhansa Cindy Inka Sari Cinthia Vairra Hudiyanti Civica Moehaimin Dhewanty Deby Chintya Dellia Airyn Delpiero, Rangga Raditya Dewi, Buana Dhan Adhillah Mardhika Dian Eka Ratnawati Diva, Zahra Dwi Anggraeni Kuntjoro Dwi Ari Suryaningrum Dwi Damara Kartikasari Edo Fadila Sirat Eka Novita Shandra Eka Yuni Darmayanti Eti Setiawati Fadhlillah Ikhsan Fajar Nur Rohmat Fauzan Jaya Aziz Fajar Pradana Fanny Aulia Dewi Fattah, Rafi Indra Fatwa Ramdani, Fatwa Febri Ramadhani Fikri Hilman Fitra Abdurrachman Bachtiar Fitria, Tharessa Fitrotuzzakiyah, Shafira Puspa Gandhi Ramadhona Gembong Edhi Setiawan Gilang Ramadhan Hendra Pratama Budianto Husin Muhamad Imam Cholisoddin Imam Cholissodin Imam Cholissodin Imam Cholissodin Indah Larasati Indriati Indriati Indriati Irawati Nurmala Sari Irfan Aprison Irma Lailatul Khoiriyah Irma Nurvianti Irma Ramadanti Fitriyani Ismiarta Aknuranda Issa Arwani Issa Arwani Jobel, Roenrico Karina Widyawati Keintjem, Arthurito Khairunnisa, Alifah Kholifa'ul Khoirin Koko Pradityo Lailil Muflikhah Lathania, Laela Salma M Kevin Pahlevi M. Ali Fauzi M. Raabith Rifqi M. Rikzal Humam Al Kholili M. Tanzil Furqon Mahar Beta Adi Sucipto, Ekmaldzaki Royhan Mahendra Data Mahendra Data Marji Marji Masayu Vidya Rosyidah Maulana, M. Aziz Mayang Arinda Yudantiar Meilia, Vina Mimin Putri Raharyani Mindiasari, Irtiyah Izzaty Miracle Fachrunnisa Almas Moch. Khabibul Karim Mochamad Chandra Saputra Mohamad Alfi Fauzan Muhammad Arif Hermawan Muhammad Dimas Setiawan Sanapiah Muhammad Harish Rahmatullah Muhammad Khaerul Ardi Muhammad Rizkan Arif Muhammad Syaifuddin Zuhri Muhammad Tanzil Furqon Mustofa Robbani Muthia Azzahra Nadia Natasa Tresia Sitorus Nainggolan, Cesilia Natasya Nanda Agung Putra Nashrullah, Nashrullah Nelli Nur Rahma Ni'mah Firsta Cahya Susilo Nihru Nafi' Dzikrulloh Noval Dini Maulana Novanto Yudistira Nur Intan Savitri Bromastuty Nurfansepta, Amira Ghina Nurhana Rahmadani Nurudin Santoso Nurul Hidayat Oky Krisdiantoro Olive Khoirul L.M.A. Panjaitan, Mutiharis Dauber Pindo Bagus Adiatmaja priharsari, diah Purnomo, Welly Putra Pandu Adikara Putra, Octo Perdana Putri, Rania Aprilia Dwi Setya Rachmatika, Isnayni Sugma Radifah Radifah Rafely Chandra Rizkilillah Rahmadi, Anang Bagus Rahmat Faizal Raissa Arniantya Ramadhianti, Fatiha Randy Cahya Wihandika Ratna Candra Ika Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Rekyan Regasari MP, Rekyan Regasari Rendi Cahya Wihandika Retiana Fadma Pertiwi Sinaga Revanza, Muhammad Nugraha Delta Revinda Bertananda Reza Wahyu Wardani Rhobith, Muhammad Ridho Agung Gumelar Rima Diah Wardhani Rinda Wahyuni Rizal Setya Perdana Rizal Setya Perdana Rizki Agung Pambudi Rizky Haqmanullah Pambudi Robih Dini Rosi Afiqo Rudito Pujiarso Nugroho Rudy Usman Azzakky Ryan Mahaputra Krishnanda Sabriansyah Rizkiqa Akbar Santoso, Nurudin Satrio Hadi Wijoyo Shelly Puspa Ardina Sigit Adinugroho Silfiatul Ulumiyah Sintiya, Karena Siti Fatimah Al Uswah Siti Utami Fhylayli Sri Wahyuni Suryani Agustin Sutrisna, Naufal Putra Sutrisno Sutrisno Tahajuda Mandariansah Talitha Raissa Tibyani Tibyani Tri Afirianto Tria Melia Masdiana Safitri Ulfah Mutmainnah Vina Meilia Wayan Firdaus Mahmudy Wildannantha, Jawadi Ahmad Yerry Anggoro Yosendra Evriyantino Yuhand Pramudita, Rezzy Yuita Arum Sari Yuita Arum Sari Yulfa Hadi Wicaksono Zubaidah Al Ubaidah Sakti