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Sutrisno Sutrisno
Fakultas Ilmu Komputer , Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Implementasi Integrasi K-Means dan Naive Bayes dalam Identifikasi Tingkat Risiko Reksa Dana Kukuh Wicaksono Wahyuditomo; Imam Cholissodin; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mutual funds are investment instruments that collect investors' funds, to be invested in securities within the mutual fund itself, with general parameters such as Net Asset Value (NAV) and time-bound returns. Both of these parameters have varying values, so they can act as a risk measure that can affect the profit of mutual funds. The effect of this risk makes people hesitate to invest in mutual funds because the level is not known based on those two parameters so that identification is involved to help determine the level of risk for mutual funds, which in this study used the integration of K-Means and Naive Bayes. The K-Means algorithm as a clustering algorithm is used to group mutual funds which then the results of the group into data classes to be classified by the Naive Bayes algorithm. The study used 250 mutual funds data on September 1, 2020, from the types of stock, money market, and mixed mutual funds. This study tested the number of clusters and the percentage amount of training data and test data. The test results showed that the optimal number of clusters was 4 with a global Silhouette Coefficient of 0,46448 and average of all classes from the evaluation of the classification model based on the best data amount percentage involving 4 classes in the form of precision of 0,9813, recall of 0,9818, and F-measure of 0,9808.
Klasifikasi Masa Panen Varietas Unggul Kedelai menggunakan Support Vector Machine (SVM) Abas Saritua Gultom; Muhammad Tanzil Furqon; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Soybean is an agricultural commodity that is very much needed in Indonesian, because soybeans are widely consumed in various food products, soybeans are also used as industrial raw materials. Soybean farmers need to know what type of soybean plant is included in the seeds to be planted, so that the increase in soybean production is maintained. To facilitate the process, data from various types of soybeans will be used. The research will be conducted using the SVM (Support Vector Machine) method because the SVM method can generalize high without having to have additional datasets. In this study, there were 6 variables and objects belonging to 3 classes, namely early age, medium age, and deep age. The best test results use a polynomial degree 2 kernel, using the lamda (λ) value of 10, Constant 1, Epsilon 0.01 and iter max of 10. Based on various tests and scenarios that have been carried out, the best evaluation value is generated in tests using K-Fold Cross Validation with a value of K = 5 and produces an accuracy value of 56.666%.
Deteksi Iklan pada Twit menggunakan Metode Naive Bayes Thariq Muhammad Firdausy; Putra Pandu Adikara; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 1 (2022): Januari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Twitter is one of the social media whose users are increasing every day. With the ease of spreading information through Twitter, some people use Twitter as a place to promote their wares by writing advertisements. These ads for some Twitter users tend to be annoying, because they are irrelevant to the information they want to read. These ads can be classified so that they can separate advertising tweets from non-advertising tweets. The ad tweet classification process is carried out using the Naive Bayes method. In the classification process, tweet data is collected to be used as training data and test data, then preprocessing is carried out on the tweet data which will then be weighted using the term frequency method. The features used in the classification with Naive Bayes are the bag-of-word feature, the textual feature is time and link, and the numeral feature is money and phone number. The classification results are obtained from the comparison of the posterior results obtained from each class. The performance level of the results obtained by the Naive Bayes method using the bag-of-word feature has a precision value 0,96, recall 1, f-measure 0,98, and accuracy 0,98.
Implementasi Sistem Pengelolaan Donasi, Kegiatan, dan Relawan Komunitas Sosial Turun Tangan Malang berbasis Web dengan Framework Codeigniter Axel Iskandar; Agi Putra Kharisma; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 10 (2022): Oktober 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Turun Tangan Malang Community has various social activities, such as health socialization, teaching and learning activities to schools in need, fundraising for areas affected by disasters, skills training for rural communities, and so on. In a previous study by Muhammad Rifqi Ramadhani, research and information gathering on the Turun Tangan Malang community was carried out, and several problems were found, the first being the need for funds to carry out community activities. Furthermore, the limitations of human resources from each activity held. Data collection from volunteers is also not well coordinated and maximal. With these problems, the author wants to carry out the implementation process of system analysis and design that has been done by previous researchers, because the author wants to prove that the designs that have been carried out by the previous thesis can be implemented on a website-based application, and help the problems that exist in the Turun Tangan Malang community. In this thesis, the analysis and design process uses previous research as a reference and an interview process is carried out for data validation. For the implementation, the author uses the PHP programming language and the Codeigniter framework on the grounds that it can facilitate the author in implementing the system from the previous thesis design. The testing process is carried out using unit testing, integration, with the white-box testing method and validation using the black-box testing method, as well as compatibility testing which shows the system can run on various browsers.
Klasifikasi Stres berdasarkan Unggahan pada Media Sosial Twitter menggunakan Metode Support Vector Machine dan Seleksi Fitur Information Gain Jeowandha Ria Wiyani; Indriati Indriati; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stress can happen to anyone and prolonged stress can cause mental health problems. However, many people continue to be unwilling to consult with mental health professionals about their concerns instead opting to complain on social media, such as Twitter. Many people use Twitter to vent their frustrations, making it possible to utilize text classification to determine someone's stress level from their tweets. In this work, the Support Vector Machine technique with Information Gain feature selection is used for text categorization. The data used in this study were 87 documents with details of 29 'Heavy' class documents, 29 'Medium' class documents, and 29 'Light' class documents. With a k value of 5, the test was run using the K-Fold Cross Validation method, and the distribution of training and test data was 80:20. The comparison of the results between the Support Vector Machine method alone with the combination of the Support Vector Machine and Information Gain methods produces the best accuracy on the Support Vector Machine method alone with an accuracy of 59.11%, precision of 29.99%, recall of 38.67%, and f-measure of 33.53%.
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