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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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Optimasi Gizi Bahan Makanan dan Paket Herbal Kayu India pada Remaja untuk Pencegahan terhadap Covid 19 serta Varian Barunya dalam upaya Meningkatkan Imunitas dan Prestasi menggunakan Algoritma Genetika Aqmal Maulana Tisno Nuryawan; Imam Cholissodin; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 6 (2022): Juni 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The Covid-19 pandemic is the most difficult time faced by the community and health workers, prevention of the virus is also increasingly being done to reduce the rate of infection. It is very important to increase the body's resistance or immunity. Therefore, in this study, the problem to be solved is regarding the optimization of nutrition for foodstuffs and herbal packages of Indian wood for adolescents for the prevention of COVID-19 and its new variants in an effort to increase immunity and performance using genetic algorithms. Genetic algorithm is a heuristic search algorithm that uses the mechanism of biological evolution. Some resistance that can be traversed by genetic algorithms is that information will be combined randomly. The following will also be matched Back regarding individuals with previous iterations. Then it will produce a minimum and maximum function to determine the price and get the fitness value as a price reference. The most optimal parameter values are obtained in the generation of 800, using a crossover reproduction of two cut points of 0.5, scrambler mutation value of 0.9, and a population of 125 by obtaining optimal parameter values, then the patient can get the best food ingredients. From the parameter values that have been obtained, the food package is optimally tested with the average nutritional requirement for patient G of 3.53%, patient K of 1.43%, patient E of 3.85%, and patient N of 4.15%, with each price obtained is Rp. 67,945, Rp. 76,397, Rp. 58,853, Rp. 58,195, in the order according to the patient.
Segmentasi Pelanggan menggunakan Metode Kernel K-Means (Studi Kasus: Smartlegal.id) Elmira Faustina Achmal; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 6 (2022): Juni 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Smartlegal.id is a company engaged in the field of law. As of 2021, SmartLegal.id has taken care of more than 60,492 business legalities throughout Indonesia since 2014. According to Smartlegal.id's Product Specialist Manager, analysis of client behavior with the aim of forming marketing strategies is still experiencing time constraints, because it requires scheduling face-to-face meetings with clients. In addition, the marketing problem that often occurs is the alignment between content, events, promos and also market needs which can take a long time to match and align. Based on these problems, a practical analysis of client behavior is needed to save time. One technique that can be used is clustering. The K-Means method is a method that has been widely implemented by Information Technology practitioners. However, with the high dimensions of the existing data, it is necessary to adjust the method by adding a kernel function so that it can better classify non-linearly separable data. From the results of the research conducted, the best Silhouette Score was 0,9035 using 2 clusters, the Polynomial kernel function with the Polynomial degree parameter was 30, and the data percentage was 100%. This study also conducted a comparison of the effectiveness between K-Means and Kernel K-Means in segmenting.
Prediksi Pergerakan Harga Cryptocurrency Bitcoin terhadap Mata Uang Rupiah menggunakan Algoritme LSTM Maulana Ahmad Maliki; Imam Cholissodin; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 7 (2022): Juli 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Investment is not new thing for most people, especially bitcoin which is very popular in recent years. Understanding the trend of price movements in investing is very important insight for investors to minimize investment risk, but predicting trend changes is very difficult challenge because it has fluctuating difference in value. The value of the increase and decrease in price of bitcoin is influenced by uncertainty factors such as political problems, economic problems at global level. So, need an algorithm that can predict prices in the future which is one strategy to maximize profits in investing. This study performs several processes to predict bitcoin price movements including, pre-processing, normalization, training the Long-Short Term Memory (LSTM) algorithm, evaluating regression matrix using Mean Square Error (MSE). Based on the results of tests that have been carried out in this study, LSTM algorithm can predict bitcoin price movements as evidenced by the MSE evaluation matrix value of 0.00374 with test parameters including 64 hidden_size, 18 sequence data, optimizer Adam, learning_rate of 0.005, and epoch 200. This research also involves several weight updated algorithms including Stochastic Gradient Descent (SGD), Stochastic Gradient Descent with Momentum (SGDM), and Adaptive Moment Estimation (ADAM) to find optimal prediction results.
Prediksi Harga Bitcoin berdasarkan Data Historis Harian dan Google Trend Index menggunakan Algoritme Extreme Learning Machine Panji Husni Padhila; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 7 (2022): Juli 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Bitcoin has attracted a lot of attention from the media and investors, given its innovative features such as its decentralization and traceability. In some countries have accepted Bitcoin as a means of payment. Bitcoin is also commonly used as an investment asset although it is quite dangerous because the price of Bitcoin is very volatile, which means that the price can go up and down quickly in a short time. In addition, Bitcoin is also believed to be speculative, whose price goes up and down depending on people's views of the coin. This study aims to predict the price of Bitcoin using the Extreme Learning Machine (ELM) algorithm based on daily historical data by considering its speculative nature using the Google Trend Index. Based on the results of the tests carried out, the results of the Mean Absolute Percentage Error (MAPE) calculation are 3,089% using the Sigmoid activation function, 5 features, 20 neurons in the hidden layer, and using Google Trend Index with the keyword 'Bitcoin'.
Prediksi Potensi Pengidap Penyakit Diabetes berdasarkan Faktor Risiko Menggunakan Algoritme Kernel K-Nearest Neighbor Renata Rizki Rafi` Athallah; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 8 (2022): Agustus 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Diabetes is a chronic disease characterized by high blood sugar. As of 2011, there were 7.29 million people suffering from diabetes, and in 2021, there were 19.47 million people who have diabetes. The percentage increase in people with diabetes from 2011-2021 has a percentage increase of 267%. Very rapid growth and one of the causes of death worldwide is a problem that needs to be solved. Reduce the number of people with diabetes, there are various ways, but they are not optimal. So it is necessary to research to develop a system that can detect diabetes early so that treatment or prevention can run well. One of the techniques that can be used to detect diabetes early is prediction. The K-Nearest Neighbor (K-NN) algorithm is an algorithm designed to classify data based on previously classified learning data however this algorithm has a weakness in processing data that has high dimensions and is non-linearly separable, so adding a kernel function is a good choice for input data clustering. From the results of this study, the value of k and the kernel function with the highest accuracy value is k = 50. The kernel function Linear and Polynomial degree 1 and the performance of the Kernel K-Nearest Neighbor algorithm are better than the K-Nearest Neighbor algorithm with a difference in the accuration value of 0.14.
Peramalan Kasus Positif COVID-19 di Jawa Timur menggunakan Metode Hybrid ARIMA-LSTM Rowan Rowan; Lailil Muflikhah; Imam Cholissodin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

COVID-19 (Coronavirus Disease 2019) is a new type of disease related to the same virus family as Severe Acute Respiratory Syndrome (SARS) and several strains of the common cold virus. Along with the increase of positive cases, the resources needed in handling COVID-19 cases also increase. To overcome this problem, anticipatory measures are needed so that the resources needed in handling COVID-19 such as health workers and medicines will be available before positive cases spike. In this study, the method used is the hybrid Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) method. The ARIMA-LSTM model is built by combining the ARIMA (2,1,2) model with the LSTM model which has 4 hidden states and 1 layer. ARIMA model is used to predict the trend value from time series data while LSTM model is used to complete the ARIMA model forecasting by predicting the time series residual value. Based on testing, the ARIMA-LSTM model achieved high accuracy, especially in short-term forecasting with an error rate of 1.8 percent for forecasting cases for the next 3 days.
Prediksi Penerimaan Mahasiswa Baru dengan Menggunakan Metode Extreme Learning Machine (ELM) (Studi Kasus pada Universitas 17 Agustus 1945 Surabaya) Aulia Jasmin Safira; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Admission of new students is a routine activity carried out by all educational institutions in Indonesia every year, which is a reflection of the public's views and interests in the educational institution. Predictions of the development of new student admissions so far have only been made based on speculation using data from previous years. An Extreme Learning Machine (ELM) is one of the methods that can be used to predict good results. Therefore, this study used the Extreme Learning Machine (ELM) method. The results of the trial in this study showed that the ELM method has a good error value measured by an error rate using the Mean Absolute Percentage Error (MAPE) of 0,20% with a comparison of the amount of training data and testing data of 90%:10%, the input weight range between-0.5 and 0.5, the number of neurons in the hidden layer as many as 2, using the Binary Sigmoid activation function, and using the number of features 2. This proves that using the Extreme Learning Machine (ELM) method, it can predict new student admissions well and get the number of new student admissions in the future.
Analisis Sentimen Tweet Covid-19 Varian Omicron pada Platform Media Sosial Twitter menggunakan Metode LSTM berbasis Multi Fungsi Aktivasi dan GLOVE Alfen Hasiholan; Imam Cholissodin; Novanto Yudistira
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

SARS-CoV-2 virus, also known as COVID-19, has become a very deadly epidemic for the past 2 years. At the end of 2021, the world was threatened by the emergence of a new Covid-19 variant, namely omicron. This variant is referred to as one that is very fast in transmission. The virus was first detected in South Africa and was designated by the world health agency (WHO) as a variant of concern under the name B.1.1.529. This has made omicron a big topic of discussion throughout the world community until now. Social media have played a crucial role in spreading information about the variant of omicron throughout the world. Twitter is a microblogging social media platform that is very effective in sharing lots of information. The number of tweets uploaded every minute is very large, up to 350,000 tweets. This number can be a very useful source of data for obtaining a public opinion on certain topics, especially the covid-19 omicron-related tweets. Sentiment analysis plays an important role in this issue. By using the sentiment analysis method, these opinions can be classified into positive or negative opinions. The long-Short Term Memory algorithm is one of the methods used in classifying the sentiment of public opinion. Optimization of this model is done by using the Glove word embedding method. This method works by counting the occurrence of a word with another word and then converting it into a vector. The result of sentiment analysis using the Long-Short Term Memory and GloVe Embedding method with 100 dimensions resulted in an accuracy rate of 82%.
Prediksi Hasil Panen Tanaman Biofarmaka di Indonesia dengan Menggunakan Metode Extreme Learning Machine Zanna Annisa Nur Azizah Fareza; Imam Cholissodin; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 11 (2022): November 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Biopharmaceutical plants are a type of horticultural plants that are used as ingredients for medicines, herbs, cooking spices and cosmetic ingredients. Biopharmaceutical plants are used as alternative medicines for various diseases and are believed to increase the body's immunity by processing them into herbs and medicines. Biopharmaceutical plants as raw materials for medicines make a major contribution to Indonesia's export activities and are in high demand due to the development of the traditional medicine industry, but the yields of biopharmaceutical plants are very unstable. Therefore, to be able to estimate the yield of biopharmaceutical crops, a prediction must be made using the Extreme Learning Machine (ELM) method. The stages of making predictions with this method start from the pre-processing process, data normalization, training process, testing process, denormalization, and evaluating error values using MAPE. This method has advantages related to fast computation compared to other neural network methods. In this study, data ratio parameters were tested with holdout validation based on nested cross validation, features, and hidden neurons. From the results of the tests that have been carried out, the optimal parameter is obtained with the smallest MAPE value of 9.34%.
Pembelajaran Mesin untuk Pembuatan Dokumen Karya Sastra Indonesia Secara Otomatis menggunakan Metode Modified Long Short Term Memory (LSTM) berbasis Extreme Learning Machine (ELM) Gusti Reza Maulana; Imam Cholissodin; Bayu Rahayudi
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

Documents as important and make it easy for human work. one type of document is a literary work. Literary work is a series of writings or an idea and thought conveyed with aesthetic purposes. The literary works used in this research are words of wisdom, poetry and rhymes. All this time making literary works are written manually. This is why many of these literary works took too long to produce. The focus of this research is to apply a literary work made from the program so that it does not take a long time to make it. This study uses a Modified Long Short Term Memory algorithm based on Extreme Learning Machine for the creation of the literary work. The process of making the literary work is preprocessing, dividing training data and test data, training phase, testing phase, calculating MAPE and calculating the accuracy of the BLEU Score. In testing the number of context neurons and hidden neurons, optimal results were obtained, namely as many as 5 and 800 for the three literary works. Calculation using the BLEU Score with a value of N = 4 produces an average accuracy for words of wisdom 74.66%, poetry of 14.49% and rhymes of 73.76%.
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