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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Imam Cholissodin
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Peramalan Hasil Panen Kelapa Sawit Menggunakan Metode Multifactors High Order Fuzzy Time Series yang Dioptimasi dengan K-Means Clustering (Studi Kasus: PT. Sandabi Indah Lestari Kota Bengkulu) Yessica Inggir Febiola; Imam Cholissodin; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 12 (2019): Desember 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Based on the export volume data recorded by the Ministry of Agriculture from 2012 to 2016, palm oil (Elaeis guineensis Jacq) became one of the centres of Government and investor attention. In the oil palm plantation company, one of which is PT. Sandabi Indah Lestari Bengkulu occurs constraints on palm oil crops that do not match the target expected. This target is the sum of harvest and when it is used to harvest the oil palm. When crops do not match the target, it can cause budgets of production that do not match the planned. Therefore, the company requires forecasting the crops to minimize the constraints caused by the crops that are not suitable for the target. The Multifactor High Order Fuzzy Time series method that is optimized with K-means Clustering to determine which subintervals are used can help in forecasting the oil palm harvest. The plot of this method is the determination of the Universe of discourse, the determination of the number of clusters, the formation of subintervals with K-means Clustering, the formation of the fuzzy set, the fuzzification process, the formation of Fuzzy Logic Relationship (FLR), and the defuzzification process. This research uses several factors that lacte over the harvest of oil palm Lot month, land area, age of the plant, and the amount of oil palm trees. From the results of the tested the sum of clusters, the sum of orders, the sum of training data, and the optimal threshold in the successive are 8, 6, 107, and 6 with the best AFER value of 36,98%.
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.
Implementasi Metode Extreme Learning Machine pada Klasifikasi Jenis Penyakit Hepatitis berdasarkan Faktor Gejala Salsabila Multazam; Imam Cholissodin; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 3 (2020): Maret 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Virus infection becomes very serious problem in medical world. Currently there are many viruses in Indonesia, including Human Immunodeficiency Virus (HIV), Novel Coronavirus (COVID-19), Dengue Virus (DENV), and Hepatitis A&B (HVA & HVB). It was recorded that in 2014 Hepaitits sufferers increased every year for the population aged above 15 years that is Hepatitis A (19.3%) and Hepatitis B (21.8%). To highly pay attention to the disease is very curcial considering Hepatitis sufferers often do not know already got infected by hepatitis. In this thesis the researchers is classifying Hepatitis types based on their symptoms using ELM method.The data being used is primary one gotten from the documents of patients infected by Hepatitis. There are 100 data with 20 features and 2 classes, namely Hepatitis A and Hepatitis B. This research was conducted in several stages from data normalization, followed by training process of the obtained data and then finally to verify the tested data input as well as data from the training process result. Based on the test results, the best ratio between train data and test data is 80: 20. This study uses several parameters to get optimal results including using 7 Hidden Neurons and the activation function used by Sigmoid Binary. By using these parameters obtained an average accuracy of 80.00%. It can be concluded that the use of the Extreme Learning Machine method can solve classification problems quite well.
Klasifikasi Jurusan Siswa menggunakan K-Nearest Neighbor dan Optimasi dengan Algoritme Genetika (Studi Kasus: SMAN 1 Wringinanom Gresik) Vergy Ayu Kusumadewi; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 4 (2020): April 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Majors is the process of selecting and placing study programs that are suitable for students, this process will affect the future of students, both when they become students in high school and after graduating when continuing their studies in collage. Based on the results of interviews the problems that often occur there are some students who want to change majors in the middle of the semester, this is because of students cannot follow their lessons and feel left behind by their friends. Therefore, we need an intelligent system that can facilitate the school in grouping students into majors in accordance with the interests and talents of students. In this research the system was made by applying the K-NN method and genetic algorithm optimization. The type of validation used in this research utilizes 9-fold cross validation and hold-out validation. The number of datasets which originally consisted of 288 data will be divided into 9 sections and each sections will amount to 32 data. In general, the best fold number to use is 10, but the share of fold must also be adjusted to the amount of data used. The hold-out test is divided into 2 test scenarios, the first is testing uses the polynomial kernel formula, the RBF kernel and the linear kernel which are elaborated (substituted into the the elaborated distance formula) get a fitness value of 64.338% while the second is testing uses the polynomial kernel formula, kernel RBF and linear kernel which are not elaborated (without substituted into the elaborated distance formula) get a fitness value of 93.182%. The highest fitness value is generated in the 9-fold cross validation test which is 100%.
Penerapan Support Vector Regression dan Particle Swarm Optimization untuk Prediksi Jumlah Kunjungan Wisatawan Mancanegara ke Daerah Istimewa Yogyakarta Rien Difitria; Imam Cholissodin; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 5 (2020): Mei 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The tourism sector is a contributor to national income, foreign exchange and a large provider of employment for Indonesia. With the increase in the number of foreign tourist arrivals and the value of foreign exchange tourism can strengthen the Rupiah exchange rate against the US dollar. Yogyakarta region still contributes a small foreign exchange tourism sector which is only 1.2% of all regions in Indonesia. There was an increase in visitors in 2011 which touched 508,476 visitors where in the previous year it only reached 368,906 visitors. Increasing the number of visitors accompanied by facilities and infrastructure that is inadequate or inadequate to the expectations of tourists can result in a decrease in visitor interest in the future and can threaten the economic sector of the people of Yogyakarta. Prediction of the number of tourist arrivals to the Special Region of Yogyakarta is very necessary to know the range of the number of visits in the future, so that tourism actors can prepare operations better, optimize facilities and infrastructure, and develop better marketing strategies. Prediction in this study uses the Support Vector Regression (SVR) and Particle Swarm Optimization (PSO) methods. Prediction results from this study produce the best range of SVR parameters from Complexity (C) = 100-500, Sigma (s) = 5-20, Lamda (l) = 1-5, Epsilon (e) = 0,0001-0.1 , cLR = 0.001-0.1 iteration SVR = 500, Particles = 30, PSO iteration = 50, number of features = 3 and number of prediction periods of 1 month by producing the smallest mean Absolute Percentage Error (MAPE) value of 1.088%. The MAPE value produced in this study is less than 10% so this prediction is able to predict the number of foreign tourist visits to Yogyakarta Special Region very well.
Klasifikasi Penyakit Tuberkulosis (TB) menggunakan Metode Extreme Learning Machine (ELM) Vivin Vidia Nurdiansyah; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 5 (2020): Mei 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Tuberculosis (TB) is the highest cause of death in the world. This disease attacks the respiratory system and included in infectious diseases. In 2019, Indonesia occupies the third highest position for the number of TB disease case namely as many as 842.000 cases. The increase in TB cases from year to year is due to people with insufficient information about the dangers as well as the treatment and prevention of this disease. Therefore, it is necessary to do the classification of tuberculosis for the community in order to determine the risk of developing TB disease based on symptoms experienced. From these problems, it is necessary to classify TB disease as an effort to increase public awareness of the TB disease. This study aims to obtain the result of the TB classification using the Extreme Learning Machine (ELM) method. Based on the result of testing and analysis using confusion matrix using TB data from Dinoyo Puskesmas in 2018-2019, the highest accuracy value is 99.33% with the number of hidden neurons 20, the percentage of training data and testing data is 70% : 30%, and uses the sigmoid biner function activation.
Klasifikasi Penyakit Dental caries menggunakan Algoritme Modified K-Nearest Neighbor Windy Adira Istiqhfarani; Imam Cholissodin; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 5 (2020): Mei 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Dental caries or commonly called cavities is a disease in which bacteria can damage the structure of dental tissue such as enamel, dentin, and cementum. Cause of occurrence dental caries is demineralization of tissue on the surface of the teeth of organic acid caused of foods that contain sugar. Caries that are not treated or controlled early can cause tooth decay that is getting worse and eventually tooth extraction. To find out more about the class of caries disease, a dental caries classification system was created using the Modified K-Nearest Neighbor (MKNN) algorithm. This method is a method that developed from the KNN method. The difference between the KNN algorithm and MKNN is calculation of the validity of training data and weight voting. In this study there were 6 classes and 8 symptoms or variables used. The test results of this study include testing the effect of k values, testing the effect of the amount of training data and test data, and its effect on distance. The results of the average accuracy of testing the value of k by 86% with the highest average of 90.66% when k = 3. Testing the effect of the amount of training data get an average accuracy of 71.1% with the highest accuracy of 86.7% on the amount of training data of 70 and for testing the effect of the amount of test data get an average accuracy of 82.2% with the highest accuracy of 86.7% on the amount of test data of 30. Testing the effect of distance get the same results of accuracy in the distance of Manhattan and Minkowski by 86.7%.
Prediksi Permintaan Keripik Buah dengan Metode Jaringan Syaraf Tiruan Backpropagation (Studi Kasus: CV. Arjuna 999) Benita Salsabila; Imam Cholissodin; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 6 (2020): Juni 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The production of fruits in Indonesia tends to increase on a yearly basis. During harvest season, so much of those fruits would be left unsold or left to rot even though the sales value would also be lower than usual. Thus, a way to preserve the shelf-life of those fruits are needed so that they would not lose their value as quickly. One way to preserve fruits is to process them into dried fruit snacks, which is the expertise of CV. Arjuna 999 located in Batu, East Java. However, the process of turning real fruits into dried fruit snacks takes a while, which is why a strategy plan is needed to anticipate rising demands and the time it takes to make dried fruit snacks. The prediction uses an artificial neural network method, backpropagation. The dataset used contains of monthly dried fruit snacks demands of CV. Arjuna 999 starting from 2017 until 2019, with 80% of overall data used as training data while the other 20% is used as testing data. The result is a MAPE score of 4.429% which was derived from a combination of parameter values such as 10 (9 + 1 bias) hidden neurons, a learning rate value of 0.8 and a maximum iteration of 900.
Pengelompokan Daerah Berpotensi Transmigrasi Menggunakan Metode Improved k-Means (Studi Kasus: Kabupaten Malang) Siti Mutdilah; Imam Cholissodin; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 6 (2020): Juni 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The transmigration program implemented by the government is not only for the equal distribution of population, but also for economic development and inter-regional development in Indonesia. Malang Regency is currently still opening a transmigration program, but many potential areas for transmigration in Malang Regency are not comparable with the transmigration quota provided by the government as well as the community's interest in transmitting transmigration. Therefore, it is necessary to group regions in Malang Regency that have potential to transmigrate in order the government can provide accurate information for people in Malang Regency and could see the potential of each region. In this study the method used is Improved k-Means which is able to improve the accuracy of clustering results and the quality of clustering when compared with the k-Means method. It is because the initial centroid selection in the Improved k-Means method is done by selecting value of the farthest distance between data. Based on the results of testing conducted the Improved k-Means method produces better cluster quality compared to the k-Means method, in the silhouette coefficient value of 0.29135900558873151 with parameters of the number clusters of 3 and maximum iteration of 3. Clusters 1 there are 4 districts with no potential transmigration, cluster 2 there are 27 districts that have the potential to transmigrate, and cluster 3 there are 2 districts which have the potential to transmigrate.
Klasifikasi Jenis Berita pada Sosial Media Twitter menggunakan Algoritme Support Vector Machine (SVM) Faturrahman Muhammad Suryana; Imam Cholissodin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 6 (2020): Juni 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is a social media that is still very popular in Indonesia. Not just for communication, Twitter now become one of the fastest way of spreading information. One of information that disseminated by Twitter is news. This thing is proven by the large number of followers in online news media's Twitter account such as @detikcom that has over fifteen million followers on its Twitter account. Nowadays, news tweets on Twitter are not categorized into categories based on the discussion in that news. This research is conducted to classify the categories of the news on Twitter to make user easily find the category of the news that users want to find. One of algorithm that can be applied to do classification is Support Vector Machine (SVM). This research use multi-class SVM algorithm with one against all method with classes as many as 5 type of classes. Before proceeding to SVM algorithm process, preprocessing and term weighting is processed first. Parameter-parameter that are used by this research are ratio of training data and test data 90%:10%, lambda = 0.1, complexity = 0.001, learning rate = 0.0001, and epsilon = 0.0001. The average accuracy value in this research is 0.85.
Co-Authors Achmad Arwan Adam Syarif Hidayatullah Adhipramana Raihan Yuthadi Adhitya Wira Castrena Adinugroho, Sigit Ageng Wibowo Agus Wahyu Widodo Aldino Caturrahmanto Alfen Hasiholan Alif Fachrony Ana Holifatun Nisa Anandita Azharunisa Sasmito Andika Eka Putra Andriko Hedi Prasetyo Anggi Novita Sari Anim Rofi'ah Annisa Alifia Annisaa Amalia Safitri Aqmal Maulana Tisno Nuryawan Ardiansyah Setiajati Arief Andy Soebroto Arina Indana Fahma Arsti Syadzwina Fauziah Atika Anggraeni Aulia Dinia Aulia Herdhyanti Aulia Jasmin Safira Azmi Makarima Yattaqillah Bahruddin El Hayat Bana Falakhi Bayu Andika Paripih Bayu Rahayudi Benita Salsabila Bisma Anassuka Bondan Sapta Prakoso Brendy Oscar Munthe Brigitta Ayu Kusuma Wardhany Budi Darma Setiawan Budi Santoso Candra Dewi Cindy Cynthia Nurkholis Citra Nadya Dwi Irianti Daisy Kurniawaty Danastri Ramya Mehaninda Daneswara Jauhari Daniel Agara Siregar Dellia Airyn Diah Priharsari Dian Eka Ratnawati Dieni Anindyasarathi Dinda Adilfi Wirahmi Diva Kurnianingtyas Dyah Ayu Wahyuning Dewi Edy Santoso Ega Ajie Kurnianto Elisa Julie Irianti Siahaan Ellita Nuryandhani Ananti Elmira Faustina Achmal Ema Agasta Ema Rosalina Eriq Muh. Adams Jonemaro Ersya Nadia Candra Fahri Ariseno Faizatul Amalia Faturrahman Muhammad Suryana Fayza Sakina Maghfira Darmawan Febriyani Riyanda Felicia Marvela Evanita Fendra Gunawan Ficry Agam Fathurrachman Fikhi Nugroho Fildzah Amalia Firda Priatmayanti Fitra Abdurrachman Bachtiar Franklid Gunawan Galih Ariwanda George Alexander Suwito Ghulam Mahmudi Al Azis Gregorius Dhanasatya Pudyakinarya Guruh Adi Purnomo Gusti Reza Maulana Heny Dwi Jayanti Heru Nurwarsito Himawat Aryadita Holiyanda Husada Husin Muhamad I Gusti Ayu Putri Diani Ibnu Rasyid Wijayanto Ichwanda Hamdhani Ika Oktaviandita Indriati Indriati Irma Lailatul Khoiriyah Ishak Panangian Sinaga Istiana Rachmi Izzatul Azizah Jeffrey Junior Tedjasulaksana Khairinnisa Rifna Khairiyyah Nur Aisyah Komang Anggada Sugiarta Kresentia Verena Septiana Toy Kukuh Wicaksono Wahyuditomo Laila Restu Setiya Wati Lailil Muflikhah Leni Istikomah Liwenki Jus'ma Olivia M. Ali Fauzi M. Khusnul Azhari Mahendro Agni Giri Pawoko Marji Marji Maulana Ahmad Maliki Maulana Putra Pambudi Mauldy Putra Pratama Mentari Adiza Putri Nasution Michael David Moch Bima Prakoso Moh. Ibnu Assayyis Mohammad Aditya Noviansyah Mohammad Angga Prasetya Askin Mohammad Toriq Muhammad Aghni Nur Lazuardy Muhammad Dio Reyhans Muhammad Fahmi Hidayatullah Muhammad Fuad Efendi Muhammad Halim Natsir Muhammad Hasbi Wa Kafa Muhammad Hidayat Muhammad Maulana Solihin Hidayatullah Muhammad Nadzir Muhammad Rizal Ma'rufi Muhammad Rois Al Haqq Muhammad Shafaat Muhammad Syafiq Muhammad Tanzil Furqon Muhammad Taufan Mukh. Mart Hans Luber Nabila Lubna Irbakanisa Nabilla Putri Sakinah Nadia Natasa Tresia Sitorus Nadia Siburian Nadiah Nur Fadillah Ramadhani Nining Nahdiah Satriani Noerhayati Djumaah Manis Novanto Yudistira Novirra Dwi Asri Nur Afifah Sugianto Nur Firra Hasjidla Nurul Hidayat Nurul Inayah Obed Manuel Silalahi Panji Husni Padhila Priscillia Vinda Gunawan Putra Pandu Adikara Putri Ratna Sari Radita Noer Pratiwi Randy Cahya Wihandika Ratih Kartika Dewi Rayhan Tsani Putra Renata Rizki Rafi` Athallah Restu Fitriawanti Reyvaldo Aditya Pradana Reza Aprilliana Fauzi Rien Difitria Rinindya Nurtiara Puteri Rio Cahyo Anggono Riski Ida Agustiyan Rizal Aditya Nugroho Rizal Setya Perdana Rizaldy Aditya Nugraha Rizky Ramadhan Rosintan Fatwa Rowan Rowan Sabrina Nurfadilla Salsabila Multazam Sandya Ratna Maruti Sari Narulita Hantari Satria Habiburrahman Fathul Hakim Sayyidah Karimah Shafira Eka Aulia Putri Shelly Puspa Ardina Shibron Arby Azizy Shinta Anggun Larasati Siti Mutdilah Sofi Hidyah Anggraini Stefanus Bayu Waskito Supraptoa Supraptoa Sutrisno Sutrisno Tara Dewanti Sukma Tibyani Tibyani Timothy Bastian Sianturi Tobing Setyawan Tony Faqih Prayogi Tusiarti Handayani Tusty Nadia Maghfira Uke Rahma Hidayah Uswatun Hasanah Utaminingrum, Fitri Vergy Ayu Kusumadewi Veronica Kristina Br Simamora Vinesia Yolanda Vivilia Putri Agustin Vivin Vidia Nurdiansyah Wahyu Bimantara Wanda Athira Luqyana Wicky Prabowo Juliastoro Windy Adira Istiqhfarani Yessica Inggir Febiola Yoseansi Mantharora Siahaan Yudha Ananda Kresna Yudo Juni Hardiko Yuita Arum Sari Yunico Ardian Pradana Yusuf Afandi Zanna Annisa Nur Azizah Fareza