Article Per Year (5 Year)
p-Index From 2021 - 2026
1.434
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Sutrisno Sutrisno
Fakultas Ilmu Komputer , Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 65 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 65 Documents
Search

 1   2   3   4   5 
Sistem Diagnosis Penyakit Tanaman Kentang Menggunakan Metode Fuzzy Tsukamoto (Studi Kasus Pada Balai Pengkajian Teknologi Pertanian Kota Malang) Achmad Dwi Noviyanto; Nurul Hidayat; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (854.929 KB)

Abstract

The Potato in indonesia is one of the important food, Because has several what nutrition have more so than with rice. The with is e istence of these potato crops the potato farmers have a relatively high economic value. Lately consumers who consume potatoes also tend to increase. The with that level then potato farmers will also add a lot, worried about the more diseases of potato crops that can harm the potato farmers. In potato disease control potato farmers should be able to know potato diseases and their solutions appropriately and correctly. The with application that the author made this can be made in the guidelines of the potato farmers in how to detect and know the solution of disease prevention suffered by the potato plant. In the application that the author made it can detect 10 diseases of potato plants with 37 symptoms suffered. In this application apply the concept of artificial intelligence, by using fuzzy tsukamoto method. The application that we created has been validated by e pert potato plants with a level of accuracy obtained from the system of 100%.
Peramalan Persediaan Spare Part Sepeda Motor Menggunakan Algoritme Backpropagation Danastri Ramya Mehaninda; Imam Cholissodin; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (894.551 KB)

Abstract

Motorcycle are the most used roudways transportation because they are more affordable and more efficient. Motorcycle require good maintenance to keep comfortable uses and maintain motorcycle performance so as to minimize accidents. Motorcycle maintenance can be done by replacing spare parts regularly in the workshop. To support the maintenance of motorcycle, the workshop should provide the best care services including having spare part inventory to suffice customer who maintance of motorcycle. If the workshop has sufficient spare part, the workshop can minimize the cost of ordering and can minimize the damage caused by storage for too long. There are many workshops that provide spare part replacement service such as Yamaha Motor. At Yamaha Motor is having difficulty in determining the spare part inventory for the next month. Inventory forecasting can help to determine the supply of spare part on Yamaha Motor. This research uses backpropagation algorithm for forecasting spare part inventory. The best backpropagation architecture is 9-7-1, which mean 9 input nodes, 7 hidden nodes and 1 output node. The input used is the history of spare part sales the previous month. The average MSE (error value) obtained from the test result is 0.0094506 and the smallest MSE obtained is 0.0085305 with the average difference of the actual value with the forecasting result is 6. At the smallest MSE value, the forecasting result approaches the actual value and has a pattern that almost the same.
Analisis Usability Website Keluarga Besar Mahasiswa Sistem Informasi Universitas Brawijaya menggunakan Usability Testing Riski Adam Elimade; Retno Indah Rokhmawati; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1139.721 KB)

Abstract

Student Family Information System (KBMSI) is an organization formed from Student Information System Universitas Brawijaya. KBMSI also use the website as a medium to meet the information needs for the outside community as well as students of their own Information Systems, especially for Information System students themselves. With the website KBMSI expected society and student Information Systems get ease when looking for information about KBMSI. But it still needs to be considered by looking at the level of Usability on the website of the Student Family Information System (KBMSI). One technique to find out if the website is easy to use is by testing Usability. Usability is a quality that the product or service has about the level of convenience that the service user can do whatever it wants to do in the way it is expected to do. Based on interviews and observations made by the author, found some problems that exist in the website of the Student Family Information System (KBMSI). Based on the interview also found some empty website content. This will affect the user's expectation of the content. Website Family Student Information System (KBMSI). According to Michael O. Leavitt and Ben Shneiderman (2003) in his book Research-Based Web Design & Usability Guidelines, the website format must be ensured to meet user expectations, especially with regard to navigation, content and organization. the information presented in the website has not met the expectations of users.
Implementasi Metode Backpropagation untuk Prediksi Harga Batu Bara Miracle Fachrunnisa Almas; Budi Darma Setiawan; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (925.734 KB)

Abstract

Coal is a natural resource that belongs to one of the fossil fuels. Indonesia is one of the countries with the largest quantity of coal production and export in the world. Coal becomes an important component in the running of a large-scale industrial company as an industrial fuel. Predicted coal prices are needed because coal prices released by the government usually takes a long time. Coal price data is in the form of time series. The data used is coal price data starting from January 2009 to September 2017 with trademark of Gunung Bayan I. This research discusses Backpropagation method that is used to predict the coal price. In this research, the effect of change parameter value from Backpropagation in predicting coal price it can be seen. Output generated by the system is in the form of predicted coal price in the next month. The results of the tests are, the lowest MSE (Mean Square Error) value of 0,00205284 with a combination of 10 neurons on the input layer, 10 neurons in the hidden layer, 1 neuron produced as output, learning rate of 0.1 and the number of iterations of 500.
Klasifikasi Risiko Hipertensi Menggunakan Fuzzy Decision Tree Iterative Dichotomiser 3 (ID3) Mochamad Rafli Andriansyah; Edy Santoso; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1130.024 KB)

Abstract

Hypertension is a disease where the heart and the arteries have abnormalities which is indicated by the increase of blood pressure. Hypertension can be controlled if it's handled from the early stage, however, several number of patients only earn the knowledge right after there's a complication of failures of the organs. Considering that hypertension is one of the very lethal diseases, the researchers have done researches about the classification of hypertension, one of them used Fuzzy Decision Tree with ID3 algorithm. To solve the problem about hypertension based on the available factors, the study use Fuzzy Decision Tree ID3 method to classify the risks of hypertension that have initialization stages of Fuzzy values, the calculation of Fuzzy enthropy values, and the values of information gain, as well as defuzzification to determine the result of the classification. The testing that has been carried out could result in the highest accuration value, which is 80%, derived from the testing of 30 training data dan 20 testing data, as well as the combination of the FCT and LDT value. The conclusion of the research that has been accomplished is that Fuzzy Decision Tree ID3 can solve the problems in the classification of hypertension risks quite well.
Diagnosis Hama Penyakit Tanaman Bawang Merah Menggunakan Algoritma Modified K-Nearest Neighbor (MKNN) Mohamad Yusuf Arrahman; Nurul Hidayat; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (628.309 KB)

Abstract

Red onion (Allium cepa L.) is a spice vegetable that is quite popular in Indonesia, has high economic value, serves as flavoring, and can be used as a traditional medicine ingredient. . However, obstacles encountered in the process of planting onions, one of the pests and diseases that often lead to crop failure. One method to diagnose diseases of shallot plants can be done with modified k-nearest neighbor (MKNN). The expert system of onion plant disease diagnosis using the k-nearest neighbor (MKNN) modified method can make it easier to detect diseases that attack onions based on symptoms. The k-nearest neighbor (MKNN) modified method is implemented on an expert system inference engine in order to draw conclusions based on existing knowledge on the knowledge base. Results obtained after the system accuracy test of 83.33% indicating that the modified k-nearest neighbor (MKNN) method is suitable for clove plant disease onion.
Analisis Sentimen Ulasan Video Animasi Menggunakan Metode Latent Semantic Indexing Faraz Dhia Alkadri; Yuita Arum Sari; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.999 KB)

Abstract

Animation videos are growing significantly producing tens even hundreds of titles per year. Certainly not everything were produced was interesting. Some of these videos may not be appealing to some people. To find out whether the animated videos is interesting or not, users can read the reviews given by other user about animation videos. Some sites that are intended to facilitate its users to be able give each other feed back about the animation video they have watched. From those reviews can be seen sentiment whether the review is a review that classified in to positive class sentiment or negative class sentiment. The Latent Semantic Indexing (LSI) method that adopts the Singular Value Decomposition (SVD) matrix reduction process is used to find the relevance between documents. With the LSI method helps us to be able to know the reviews are classified on positive sentiment or negative sentiment. The TF IDF method is used to process textual data into numerical data and cosine similiarity method is used to calculate the similiarity between data which is further classified into positive class sentiment and negative class sentiment. Testing done as much as 19 times by using different k-rank input. Based on the test result, this system produces an optimal accuracy on k-rank =10 that is equal to 86% so we can conclude that latent semantic indexing is good to use for searching relevance between documents.
Implementasi Naive Bayes dan Weighted Product Dalam Memberi Rekomendasi Hotel Terbaik Saat Berwisata Di Bali Galih Aulia Rahmadanu; Edy Santoso; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (669.163 KB)

Abstract

Bali is one of the best tourist destinations in Indonesia. The number of tourists coming to Bali always increases by 300.00 to 500,000 people every year. In 2017 amounting to 62.89% of tourists visiting Bali chose to stay at the hotel. But based on the wrong comments found in one of the largest hotel booking applications in Indonesia, Traveloka is still found to have complaints about hotels that are not in accordance with tourist expectations. Therefore, the hotel recommendation system is made by calculating the value for each of the points considered important in the assessment of a hotel. In this system two methods are used, namely Naive Bayes and Weighted Product. The Naive Bayes method is used to classify the input given by the user into the existing hotel category and the Weighted Product method is used to provide hotel recommendations by doing hotel ranking that is closest to the criteria that the user wants. In this system there are 7 rating points for hotels and hotels divided into 3 categories. The results of system accuracy testing using 50 hotel data resulted in the best level of accuracy of 100%.
Sistem Rekomendasi Perbaikan Jalan di Makassar Dengan Metode AHP-TOPSIS Andi Amaliyah Maryama; Nurul Hidayat; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (267.285 KB)

Abstract

Road damage is one of the many problems experienced in Indonesia, especially in the capital city, such as Makassar as the capital of South Sulawesi Province and becoming focus of problems that must be resolved by the local government Unfortunately, there is no balance between the number of damage roads and the budget. So, the government needs a system to help the government in recommending road improvements based on factors that can be quantified. This thesis will discuss the application of AHP-TOPSIS method to determine road priority recommendations to be repaired. The AHP (Analytical Hierarchy Process) method in this case will play a role in the weighting process and continue with the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to calculate the shortest distance between the alternative with the value of the positive ideal solution. AHP method includes several processes, such as normalization, calculation of eigen vector values, calculation of index value consistency and calculation of the value of consistency ratio. While the TOPSIS method includes several processes namely normalization, calculation of positive and negative ideal matrices then proceed with the calculation of the maximal measure max value and separation measure min. This system uses data from 78 data which form the basis of calculations and 10 road recommendation data which become test data. The application of the AHP-TOPSIS method to determine priority recommendations for road repair in Makassar using various factors such as: good road conditions, moderate road conditions, damaged road conditions, severely damaged road conditions and LHR (average daily traffic). From this study obtained an accuracy value of 70% due to the difference between the results of system trials with data obtained from the Public Works Office of the City of Makassar.
Prediksi Tingkat Pemahaman Siswa Dalam Materi Pelajaran Bahasa Indonesia Menggunakan Naive Bayes Dengan Seleksi Fitur Information Gain Siti Utami Fhylayli; Budi Darma Setiawan; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (388.297 KB)

Abstract

Indonesian Language Subjects are generally regarded as easy lessons and do not need to be studied by students and society. Based on this, various learning problems arose involving instructors, Indonesian language subjects, students who received lessons, teaching methods, facilities, ways to obtain, and the objectives of Indonesian language learning (Moeljono, 1989). The difference between each student in different learning differences. This causes the teacher to have limitations in measuring the level of understanding of students. Then a system is needed to predict the level of understanding of students. This prediction uses the classification method with the Naive Bayes algorithm. The class that will be used in this study is that students understand, are quite understanding and lack understanding. In this study, the authors used the Information Gain (IG) feature selection. The selected feature will be processed with the Naive Bayes classification algorithm, then the accuracy will be seen if it is not maximized, then the previous feature selection process will be done again to get the desired verification. From the tests that have been conducted, the results obtained which have a Gain value of more than 0.2 have the largest rating, reaching 90%. The features chosen from 17 included features of family members, residence status, mother's work, caregivers, family support, joining extracurricular activities, repeating lessons at home, length of study at home, reading at home, reading time at home.
Co-Authors Abas Saritua Gultom Achmad Dwi Noviyanto Adinugroho, Sigit Aditya Negara Aditya Sudarmadi Agi Putra Kharisma Agus Prayogi Ahmad Galang Satria Anandita Azharunisa Sasmito Andi Amaliyah Maryama Arthur Julio Risa Ashshiddiqi Axel Iskandar Budi Darma Setiawan Candra Dewi Chalid Ahmad Aulia Chindy Putri Beauty Cindy Inka Sari Danastri Ramya Mehaninda Deby Chintya Dewi Syafira Dhavin Putra Alamsyah Dhimas Tungga Satya Dina Dahniawati Dita Sundarningsih Dyah Ayu Wahyuning Dewi Edy Santoso Endah Utik Wahyuningtyas Enny Trisnawati Fajar Pradana Faraz Dhia Alkadri Febriyani Riyanda Filan Maula Andini Firhad Rinaldi Saputra Fran's Dwi Saputra Atmanagara Galih Aulia Rahmadanu Heru Budiyanto Ian Lord Perdana Imam Cholissodin Imam Farouqi Faisal Inas Nabila Indri Monika Parapat Indriati Indriati Jeowandha Ria Wiyani Jodi Irjaya Kartika Karuniawan Susanto Kukuh Wicaksono Wahyuditomo M. Ali Fauzi Mahardhika Hendra Bagaskara Marji Marji Miracle Fachrunnisa Almas Mochamad Ali Fahmi Mochamad Rafli Andriansyah Mohamad Yusuf Arrahman Muhammad Abdan Mulia Muhammad Alfian Nuris Shobah Muhammad Hafidzullah Muhammad Tanzil Furqon Nanda Firizki Ananta Nurul Hidayat Putra Pandu Adikara Putri Indhira Utami Paudi Rachmad Faqih Santoso Rachmad Ridlo Baihaqi Rahmatsyah Rahmatsyah Rakhmadina Noviyanti Randy Cahya Wihandika Ratih Kartika Dewi Rayindita Siwie Mazayantri Rekyan Regasari Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Retno Indah Rokhmawati Rezza Hary Dwi Satriya Rich Juniadi Domitri Simamora Riski Adam Elimade Rizal Maulana Sabrina Nurfadilla Safira Dyah Karina Siti Utami Fhylayli Supraptoa Supraptoa Thariq Muhammad Firdausy Tibyani Tibyani Tri Halomoan Simanjuntak Tunggul Prastyo Sriatmoko Wayan Firdaus Mahmudy Widya Amala Sholikhah Yose Parman Putra Sinamo Yuita Arum Sari