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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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Prediksi Curah Hujan dengan Empat Parameter menggunakan Backpropagation (Studi Kasus: Stasiun Meteorologi Ahmad Yani) Aulia Herdhyanti; Lailil Muflikhah; Imam Cholissodin
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

Rainfall is the amount of water that falls to the surface of the ground over a certain period. The rainfall itself is recorded by the BMKG (Meteorology, Climatology and Geophysics Agency) every day. According to research, rainfall is influenced by several parameters, including the temperature, the humidity of the air, the speed of the wind, and the activity of solar. In forecasting the weather, high accuracy is needed because the weather greatly affects the activities of the population. High rainfall can cause floods. Because of this problem, one solution that can solve this problem is to predict the rainfall with the backpropagation method which is one of the neural network architectures that has a multi-layer network and processes the training data forward and corrects the errors backwards. This study uses the rainfall data with parameters that influence it, namely the average temperature, the humidity of the air, the speed of the wind, and the activity of solar within 19 months from the Ahmad Yani Meteorological Station in Semarang. The best accuracy obtained with the backpropagation method is the MSE value of 0.006952 which was obtained by using 2 hidden neurons, the maximum iteration is 1000 iterations, the amount of training data is 70% of the total dataset, and the learning rate is 0.05.
Analisis Sentimen Citayam Fashion Week pada Komentar YouTube dengan Metode Convolutional Neural Network George Alexander Suwito; Imam Cholissodin; Putra Pandu Adikara
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

Citayam Fashion Week's virality started with the circulation of interview videos on Tiktok and Instagram. The positive impact was felt especially for traders due to the high number of visitors who wanted to see the event so that they could increase their turnover, apart from the positive impact felt, traffic jams arise due to the high number of visitors and the location of the fashion show action, thereby disrupting the activities of the general public. Citayam Fashion Week was disbanded on Wednesday, 27 July 2022. This caused debate among the public. Some people support the holding of the event, some people do not support holding the event. Various positive, and negative comments were given by the public to the Citayam Fashion Week event, therefore a method is needed that can automatically sort out user sentiments. and efficiently, avoiding public misperceptions of existing comments. In this study the authors used the Convolutional Neural Network (CNN) method in conducting sentiment analysis, based on the results of the tests that have been carried out, this system has the best metric evaluation value, namely an accuracy value of 97%, a precision value of 97%, a recall value of 98%, and the f-measure value of 97%.
Klasifikasi Sinopsis Novel berdasarkan Jenis Genre menggunakan Multi-class Support Vector Machine dan Chi-square Bana Falakhi; Imam Cholissodin; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 1 (2023): Januari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Novels are a well-known and frequently read form of fictional works. Novels typically have over 100 pages and are available in a wide variety of genres. However, most novels have simple covers and only containt a brief title and narrative synopsis. It is difficult to determine the genre of a novel due to the lack of genre information on the cover. By these issues, a new classification method was developed by utilizing novel synopsis data and a multi-class Support Vector Machine (SVM) algorithm with a One-Against-All strategy. The TF-IDF and Chi-square approaches are also used for term weighting and features selection. To achieve the highest classification accuracy, this work implements two SVM kernels: the linear kernel and the gaussian kernel. 240 summary texts were used as training and testing dataset, grouped into four different genre categories: horror, romance, science fiction, and history. During tests, the kernel type, Chi-square threshold value, and sequential training parameters were changed to achieve the best classification accuracy result. Based on the test results, the highest classification accuracy value of 94.58% is achieved at the Chi-square threshold of 80%, SVM with a linear kernel, sequential training parameter with lambda (λ) = 0,5, gamma (γ) = 0,05, complexity (C) = 1, epsilon (ε) = 0,0001, and the maximum number of iterations is 100.
Analisis Sentimen terhadap Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) Level 3 berdasarkan Data Twitter menggunakan Algoritma Naive Bayes Annisa Alifia; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 1 (2023): Januari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The rapid development of technology has made it easier for people to express their aspirations. These aspirations can be channeled through social media which is currently increasingly popular among the public. One of the social media that is often used by Indonesian citizens is Twitter. In February 2022, Community Activities Restrictions Enforcement Level 3 had become a trending topic on Twitter, which indicated that the increase in the level elicited various responses from the public. Community Activities Restrictions Enforcement (CARE) is one of the policies that the government has implemented in tackling Covid cases in Indonesia. Public opinion regarding this issue will generate various sentiments that can be analyzed. In this study, sentiment analysis will be carried out on public opinion regarding the increase in Community Activities Restrictions Enforcement to level 3 using the Multinomial Naive Bayes algorithm. The process consists of data pre-processing, word weighting with Raw Term Frequency, training and testing of the Naive Bayes model which later the accuracy results will be calculated using the K-Fold Cross Validation of 5 folds. This study produces an average accuracy of 0.78 with the addition of stop words and data normalization. This accuracy does not create much difference without using stopwords and data normalization with an accuracy of 0.79. The addition of stopwords and data normalization still does not produce a significant difference.
Penerapan Algoritma Long Short-Term Memory (LSTM) berbasis Multi Fungsi Aktivasi Terbobot dalam Prediksi Harga Ethereum Timothy Bastian Sianturi; Imam Cholissodin; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 3 (2023): Maret 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One form of innovation of technological development is cryptocurrencies that have been widely recognized as an alternative to currency exchange. One of the cryptocurrencies that is quite popular today is Ethereum which started trading for the first time on August 7, 2015 at a price of US$2.83 and reached its highest price on November 8, 2021 at a price of US$4822.97. Ethereum has high price fluctuations and has many factors that affect the price of ethereum such as political or economic problems at the global level so as to cause sufficient investment risk This research performs several stages in predicting ethereum price movements, namely pre-processing, data normalization, training Long Short-Term Memory (LSTM) algorithms, and evaluating with Mean Square Error (MSE). Based on the results of this study, researchers succeeded in predicting the price of Ethereum using the multi-function activation based LSTM algorithm with testing parameters for the proportion of training data and testing data of 70%:30%, the number of sequences of 14 which describes data for 14 days, the hidden unit value of 64, the number of epochs of 150, and sigmoid as an activation function as evidenced by the MSE value of 0.0121..
Prediksi Harga Cabai menggunakan Metode Long-Short Term Memory (Case Study : Kota Malang) Michael David; Imam Cholissodin; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 3 (2023): Maret 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Chili peppers are fruits or plants of the genus Capsicum. Fruits can be classified as spices or vegetables. As a spice, chili peppers are very popular in Southeast Asia as a food flavor enhancer. Chilies have some nutritional content in 100 grams of it, which content: Water, energy, protein, fat, carbohydrates, fiber, calcium, etc. Chili also has several benefits, including: Relieve pain, maintain digestive health, maintain blood sugar levels, etc. In Malang, chili prices fluctuate, so the price of chili is difficult to predict. This makes the government worried in maintaining the stabilization of chili prices so that they remain affordable and the chili price inflation in Malang City will be good. In this research, several prediction processes were carried out, including, consisting of pre-processing, data normalization, training and prediction using the Long Short-Term Memory method, and error results using the Mean Square Error (MSE). Based on the tests that have been done using daily data on cayenne pepper prices from January 1 2021 to July 31 2022 in Malang City using the Long Short-Term Memory method, the smallest MSE result is 0.0155 with a proportion of training data and testing data of 70%; 30%, with 21 sequence data, 128 hidden units and 150 epochs.
Prediksi Jumlah Kasus COVID-19 di Dunia dengan menggunakan Metode Long Short-Term Memory Adhipramana Raihan Yuthadi; Imam Cholissodin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 5 (2023): Mei 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Coronavirus 19 (COVID-19) merupakan penyakit menular yang diakibatkan oleh virus baru yang dinamakan coronavirus. Dilansir dari WHO, Virus ini pertama kali ditemukan pada bulan Desember 2019 di daerah Wuhan, Tiongkok. Dalam waktu kurang dari dua bulan, virus ini telah menyebar ke lebih dari 114 negara. Selain korban jiwa, wabah COVID-19 juga berdampak buruk kepada sektor lainya, khususnya sektor ekonomi. Selain itu kebutuhan akan informasi yang akurat juga diperlukan untuk pengambilan keputusan. Oleh karena itu, disini diperlukan dilakukan peramalan mengenai jumlah kasus COVID-19 untuk beberapa bulan kedepanya. Hal ini dilakukan agar semua pihak khususnya pemerintah mendapatkan hasil analisa yang sesuai dan akurat, sehingga dapat mempersiapkan keputusan yang terbaik dan efektif berdasarkan hasil peramalan yang didapatkan. Untuk melakukan peramalan tersebut, metode yang digunakan dalam penelitian ini adalah Long Short Term Memory dengan jumlah data sebanyak 1258 record yang terbagi kedalam 6 negara berbeda yaitu, Indonesia, China, Jepang, Singapura, Malaysia dan Korea Selatan. Dari hasil pengujian dengan pembagian persentase data dengan iterasi maksimum = 100 diperoleh nilai Root Mean Square Error (RMSE) terkecil dengan nilai 7.8773 hingga yang terbesar dengan nilai 17.6983. Untuk proses pengujian dengan variasi iterasi antara 50 hingga 500 diperoleh nilai Root Mean Square Error (RMSE) di kisaran 11.
Analisis Sentimen Pemindahan Ibu Kota Indonesia pada Media Sosial Twitter menggunakan Metode LSTM dan Word2Vec Yunico Ardian Pradana; Imam Cholissodin; Diva Kurnianingtyas
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 5 (2023): Mei 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Pemindahan ibu kota negara dari DKI Jakarta ke Pulau Kalimantan telah menimbulkan perdebatan dan meningkatkan minat publik terhadap isu tersebut. Twitter menjadi media sosial yang populer untuk menyampaikan pendapat dan aspirasi masyarakat. Oleh karena itu, penelitian ini bertujuan untuk menganalisis sentimen masyarakat terkait pemindahan ibu kota menggunakan metode analisis sentimen. Dalam penelitian ini, metode Deep Learning, khususnya Long Short Term Memory (LSTM) dan word2vec yang digunakan untuk menganalisis sentimen tweet masyarakat. Dengan menerapkan metode LSTM dengan Word2Vec, diharapkan dapat diklasifikasikan apakah tweet masyarakat bersifat positif atau negatif terkait pemindahan ibu kota. Model LSTM yang dikembangkan dalam penelitian ini menghasilkan akurasi sebesar 95%, precision sebesar 93%, recall sebesar 93%, dan F1-measure sebesar 95%. Hasil tersebut menunjukkan bahwa metode ini efektif dalam menganalisis sentimen masyarakat terkait pemindahan ibu kota dan dapat memberikan pemahaman yang lebih baik mengenai pandangan publik terhadap perubahan tersebut.
Analisis Sentimen Ulasan Pengguna Aplikasi Shopee di Google Play menggunakan Metode Word Embedding dan Long Short Term Memory (LSTM) Izzatul Azizah; Imam Cholissodin; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 5 (2023): Mei 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Perkembangan teknologi yang terus berkembang melalui ide-ide kreatif yang membantu dan memudahkan proses kehidupan salah satunya dalam aspek ekonomi. Transaksi jual beli yang memiliki sangkutan dengan ekonomi sudah mengalami digitalisasi. Penjual beserta dengan pembeli banyak yang mulai atau beralih menuju pasar online dikarenakan kemudahan serta manfaat yang didapatkan dalam transaksi ini. Hal tersebut berdampak kepada persaingan antar e-commerce yang ada. Berdasarkan dari Website data.ai pada Bulan Februari 2023, Shopee menduduki peringkat pertama pada kategori shopping dengan platform Google Play. Google Play merupakan platform layanan distribusi digital yang menyediakan fitur rate dan ulasan untuk memberikan feedback terhadap aplikasi yang tersedia. Namun, tak jarang penilaian dalam bentuk rate tidak sesuai dengan kualitas yang sebenarnya, sehingga untuk mengetahui feedback secara lebih jelas dapat dilihat dari ulasan teks pengguna. Penelitian ini melakukan analisis sentimen terhadap ulasan yang ada menggunakan metode Word2vec untuk tahap word embedding dan LSTM untuk proses klasifikasi. Beberapa tahapan yang dilakukan pada penelitian ini adalah preprocessing, word embedding, lalu proses klasifikasi dan diakhiri dengan evaluasi. Dari proses pengujian hasil akhir didapatkan nilai akurasi 0.73 dan nilai f1-score 0.82.
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