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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi (JUST-SI)
Tibyani Tibyani
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Prediksi Harga Cabai Rawit di Kota Malang Menggunakan Algoritme Extreme Learning Machine (ELM) Galih Ariwanda; Imam Cholissodin; Tibyani Tibyani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cayenne is a commodity for food that cannot be separated from the daily needs of people in Indonesia. Cayenne for the people in Malang City is consumed to maintain metabolism and body temperature to keep warm and vitamin C which can help maintain the health of the human body. Prices of cayenne in Malang City always fluctuate changes every day. Fluctuation changes that make the price of cayenne are difficult to predict well. In addition, the prices given by traders are always varied, cayenne pepper is also one of the contributing commodities of inflation and prevents the difference in prices obtained by consumers and farmers so that they are not harmed by each other. Therefore, it is necessary to predict the price of cayenne in Malang so that consumers and the government can take preventive measures against the existing problems. The prediction process is divided into several process including pre-processing, normalization of data, predictions using the Extreme Learning Machine algorithm, and the results of errors with MAPE. Based on the results of testing using cayenne price data from January 1, 2017 to December 31, 2018 in Malang City, the smallest MAPE value was 2.087% with 2 features, the number of neurons in the hidden layer was 5, the percentage of training data and testing data 90%:10%, and the activation function is Binary Sigmoid.
Implementasi Algoritme Genetika Dalam Penjadwalan Akademik Sekolah Menengah Atas Brawijaya Smart School Tobing Setyawan; Imam Cholissodin; Tibyani Tibyani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Scheduling is one of the most needed requirement for high school. Scheduling at brawijaya smart school senior high school(BSS SHS) had some problems like processing time, schedule size, Musyawarah Guru Mata Pelajaran(MGMP), and managing variety subjects. Scheduling took 3 days to be done. Problem space's size is 834 hours of subject. Teacher's schedule cannot crashed with MGMP. Variations of teachers and subjects cannot be the same within a day from those problems above researcher will use genetic algorithm to solve them. Genetic algorithm is an algorithm that can be used to get the solution that closed to optimum from the wide possibility solution area. In this research, researcher used partially-mapped crossover, reciprocal exchange mutation, and elitism selection. The result gets that the 233rd generation, 150 population size, 0,7 crossover ratio, and 0,3 mutation ratio are the most optimum solution parameter in BSS SHS scheduling case. This research has its disadvantage in early convergence that happened at 233rd generation so random injection is needed to be applied. Global and local search aren't effective because searching ratio is always the same. Population size was too big that difficults the searching for the best parent so parent selection needed to be applied.
Convolution Neural Network (CNN) Untuk Pengklasifikasian Citra Makanan Tradisional Akhmad Rohim; Yuita Arum Sari; Tibyani Tibyani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

People in this digital era take a picture before eating is one of lifestyle. Then the result of the picture will be uploaded to social media. Traditional food's pictures dissemination still less identified encourages this research about the classification of traditional food's image. Extraction of classification features food image is difficult because of food can vary dramatically in appearances such as shape, texture, color, and other visual properties. Convolution Neural Network (CNN) is a method that can learn its own features on a complex image. Hopefully, CNN evaluation results for the classification image of traditional food can provide a solution to identify the image of traditional food. Result of this research in building the architecture of the Convolutional Neural Network model for classification of the traditional food image required 4 conditional layers, 4 max-pooling layers, and 2 fully connected layers. That architecture obtained because it gets the smallest loss value with 0.000044 value on the 15 epoch during the learning process and gets a 73% precision, 69% recall, and 69% F-score.
Implementasi Metode Analytical Hierarchy Process dengan Weighted Product untuk Rekomendasi Penentuan Pegawai Terbaik berdasarkan Kinerja (Studi Kasus Divisi CCRD Bank BTN Kantor Pusat Jakarta) Irvan Windy Prastyo; Nurul Hidayat; Tibyani Tibyani
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

Employee performance is a factor that influences the results to be achieved by a company. The effort are made to achieve the target of the Bank Tabungan Negara (BTN) Consumer Collection & Remedial Division (CCRD) division on employee performance is to assess employee performance. This study aims to apply the algorithm Analytic Hierarchy Process with Weighted Product method, and test the accuracy of recommendations for determining the best employee of the BTN CCRD division. The basic method used in this research is the AHP-WP method. The Analytic Hierarchy Process (AHP) method in this study will be used for weighting criteria and followed by the Weighted Product (WP) method to calculate the alternative value of each of the criteria and provide a sequence of recommendations for employees. This system uses 60 data which form the basis of calculations with 10 test data in each position with as many as 6 positions, namely ST, STC, AFC, TLFC, CS, and BC. The application of the AHP-WP method is to determine the recommendations of the best employees of the BTN bank by using assessment criteria such as shifting, damming, restructuring, ICOLL input, assessment 1, assessment 2 and assessment 3, and each position has different criteria. The results of this study indicate that the design of employee recommendation systems using the Analytic Hierarchy Process with Weighted Product method with the system java programming language running well, with the results in accordance with the manualization calculation and obtained the highest correlation closeness which is 1, which means perfect correlation and the lowest 0.5, which means a quite thightly correlation with each of the 5 top ranking and 5 lastly data in testing.
Sistem Diagnosis Penyakit Jantung Menggunakan Metode Modified K-Nearest Neighbor Kholif Beryl Gibran; Nurul Hidayat; Tibyani Tibyani
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

Cardia is included in the unit which is very crucial for human physical. As a blood pump to each limb is the main function of the cardia. Loss of the function of the cardia tissue and abnormalities of the cardia regulator and infection can be caused by cardia failure occurs when the cardia is no longer able to meet the level of nutrition and oxygen needs of the body. Based on the report of the World Health Unit (WHO) caused by cardia disease one third of the 58 million people who died in 2005 (Afriansyah 2009). The cause of death number 1 in the world for now is cardia. Cardia disease causes at least 30% of deaths in almost the entire world or about 17 and a half million in 2005. According to the World Health Organization (WHO), while coronary cardia disease itself causes 60% of deaths due to cardia (WHO, 2007). While 26.4 percent of deaths are due to cardiovascular disease including coronary cardia, it is based on the National Census conducted in 2001 (MOH RI, 2003). According to the explanation of the problem that has been described and also according to the exposure of previous studies, therefore the appropriate title for this research is "Diagnosis System for Heart Disease Using the Modified K-NN (MKNN) Method".
Peramalan Curah Hujan Menggunakan Metode Extreme Learning Machine Rich Juniadi Domitri Simamora; Tibyani Tibyani; Sutrisno Sutrisno
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

Rainfall is the height of rain water that is found and collected in a flat, not absorbed, does not evaporate and does not flow. Information about rainfall is very important especially in agriculture and civil. In agriculture, rainfall information is used to determine the type of plants to be planted in accordance with the intensity of rainfall, predicting the start of the growing season in the planting calendar to minimize the risk of planting. In the civil field, it is used as a determinant of engineering design standards in planning flood disaster control buildings. Above normal rainfall will cause natural disasters such as floods and landslides. Rainfall is part of the weather element and one of the meteorological processes that is quite difficult to predict. Rainfall forecasting is needed so that the community and the government can take preventative measures against the existing problems. The forecasting process is divided into several processes which include data normalization, forecasting with the Extreme Learning Machine algorithm, data denormalization and the results of errors with MAPE. Based on the test results using rainfall data in the Poncokusumo area with a span of years 2002 to 2015 obtained the smallest MAPE value of 3.6852%, with as many features as 4, many neurons in the hidden layer as much as 2, the percentage of training data 90%.
Prediksi Jumlah Kunjungan Wisatawan Mancanegara Pada Negara Singapura Menggunakan Algoritme Extreme Learning Machine Muhammad Sanzabi Libianto; Tibyani Tibyani; Yuita Arum Sari
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

Tourism is one of the important aspects contributing to income for the country, especially Singapore. Singapore is ranked 2nd on the Asian continent and ranks 13th in the world in the tourism sector. One of Singapore's state revenue generated in the tourism sector is 14.8%. More than 16 million foreign tourists come to Singapore every year. However, the number of tourist visits has increased and decreased every month. Changes in the value of the fluctuations can lead to less than the maximum tourism industry in Singapore, especially the infrastructure sector. Because the accommodations are limited in accommodating a large number of tourists. Therefore, a prediction of the number of tourist visiting to the country of Singapore is needed, so that it becomes material for consideration in preparing better accommodation. One prediction algorithm that can be used is Extreme Learning Machine (ELM). From the results of the research that has been done, the optimal algorithm parameters on ELM are, the number of feature data = 5, the ratio of training data and testing data = 80%: 20%, and hidden neuron = 10 with the data on the number of tourist visiting in January to December from 2010 to 2016, the error value obtained using MAPE was 7.41%.
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.
Implementasi Algoritme Fuzzy Tsukamoto untuk Penilaian Kinerja Pegawai PT. Bank Tabungan Negara (Persero) Divisi CCRD (Consumer Collection & Remedial Division) Muhammad Resna; Nurul Hidayat; 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

Employees are people who work in a company by getting a salary (wage) from the company. The employees performance is the work of quality and quantity achieved by an employee in carrying out his duties in accordance with the responsibilities given to him. So that every company needs assessment of performance for the company to evaluate the performance of each employee in every month. At Bank Tabungan Negara (Persero) company, the CCRD division (Consumer Collection & Remedial Division) still uses the Microsoft Excel scoring system so that a system that can facilitate leaders to assess employee performance and provide a more accurate assessment are needed. The solution for making this system is by using the Fuzzy Tsukamoto method. Fuzzy Tsukamoto is used to calculate the rating of each employee in each position. Based on the accuracy testing that has been done, from 50 Skip Tracer position test data, 30 Assistant Field Collector position test data, 10 Field Collector position test data, 8 Skip Tracer Coordinator position test data, 10 Team Leader Field Collector position test data, 5 Collective Staff position test data, and 5 Branch Coordinator position test data. So from the test data, Branch Coordinator positions get accuracy of 86%, Branch Coordinator positions by 76%, Field Collector positions by 70%, Skip Tracer Coordinator positions by 75%, Team Leader Field Collector positions by 90%, Team Leader Field Collector positions by 60%, and Branch Coordinator positions by 80%.
Optimasi Travelling Salesman Problem Pada Angkutan Sekolah Menggunakan Algoritme Ant Colony Optimization (Studi Kasus: MI Salafiyah Kasim Blitar) Moh. Ibnu Assayyis; Imam Cholissodin; Tibyani Tibyani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 1 (2020): Januari 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mobility is the movement from one place to another, where in the implementation of mobility requires a tool that can support. The field associated with mobility is transportation. The use of transportation is applied in MI Salafiyah Kasim as a solution to ease the burden of guardians. Because the guardian can not every day pick up their children from school, especially the age of students who are still very young and worried about having to go or go home from school alone and distance of school and home far enough. Optimization of the school's own private transportation will be expected to bring the optimal solution to minimize constraints, such as: lack of efficiency in delivery times, traffic accidents, to save the school budget. Ant Colony Optimization (ACO) is the preferred algorithm for optimizing Travelling Salesman Problem (TSP) problems. In this research, the data is the distribution of kloter delivery of students to homes divided by 2 kloter. Where the total number of students is 44 people, the first group of 20 people and the second group of 24 people. From the test results obtained best optimization was 5,711 km (22,71%) on first cluster and 34,5551 km (62,14%) on second cluster.
Co-Authors Abi Dwijo Sukma Adinugroho, Sigit Aditya Dwi Wicaksono Adlan Husein Malahella Adnan Mahfuzhon Agung Bachtiar Sukmaarta Agung Cahya Kurniawan Ahmad Afif Supianto Akhmad Rohim Ana Holifatun Nisa Andrew Adi Nugraha Angga Dwi Apria Rifandi Anindya Agustina Damayanti Anisa Dwi Novita Rika Ardiansyah Ardiansyah Aries Suprayogi Ayang Setiyo Putri Bagus Cakra Jati Kesuma Barlian Henryranu Prasetio Belqis Putri Himmatul Karimah Bilal Benefit Buce Trias Hanggara Budi Darma Setiawan Dahnial Syauqy Davriwan Dzaky Muttaqien Diah Priharsari Edwin Yosef Setiawan Sihombing Edy Santoso Fadhlillah Ikhsan Faizatul Amalia Fitriyah, Hurriyatul Galih Ariwanda Gembong Edhi Setyawan Hazal Kurniawan Putra Ians Adji Adhitama Imam Cholisoddin Imam Cholissodin Imam Ghozali Indah Riska Aulia Irvan Windy Prastyo Irwan Kurniawan Issa Arwani Ivarianti Sihaloho Kamal Irsyadillah Kevin Aditya Firmansyah Putra Kholif Beryl Gibran Kresna Wiska Kafila Lailil Muflikhah Lb Novendita Ariadana Linda Silvya Putri Luthfi Anshori M. Adib Fauzi Rahmana M. Ali Fauzi M. Rizzo Irfan Mochammad Hannats Hanafi Ichsan Mochammad Izzuddin Moh. Ibnu Assayyis Muchammad Cholilulloh Muhammad Resna Muhammad Rizki Augusta Muhammad Sanzabi Libianto Muhammad Yusuf Ramadan Muria Naharul Hudan Najihul Ulum Nabila Divanadia Luckyana Nurul Hidayat Paul Manason Sahala Simanjuntak Pitoyo Peter Hartono Priscillia Pravina Putri Sugihartono Putra Pandu Adikara Ramadhan Anindya Guna Aniwara Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Rich Juniadi Domitri Simamora Rivan Haposan Rizal Maulana Ryan Bayu Permadi Sarah Aditya Darmawan Siti Nafiah Sutrisno Sutrisno Tegar Assyidiqi Nugroho Tobing Setyawan Tri Putra Anggara Utaminingrum, Fitri Valensiyah Rozika Widhy Hayuhardhika Nugraha Putra Yanuar Enfika Rafani Yogi Suwandy Yuita Arum Sari