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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Kubik Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JRSI (Jurnal Rekayasa Sistem dan Industri) JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Computer System and Informatics (JoSYC) KUBIK: Jurnal Publikasi Ilmiah Matematika Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering
Aniq Atiqi Rohmawati
Telkom University
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences

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Implementasi Genetic Algorithm dalam Model ARIMA untuk Memprediksi Observasi Time Series Rangga Arya Pamungkas; Indwiarti Indwiarti; Aniq Atiqi Rohmawati
Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 3 (2019): December, 2019
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2019.4.3.353

Abstract

Nilai harga saham selalu berubah-ubah dan berfluktuasi setiap harinya. Untuk menghadapi masalah mengenai ketidakpastian harga saham, perlu dilakukan suatu peramalan time series untuk memprediksi harga saham di masa mendatang. Pada penelitian ini, metode yang digunakan untuk memprediksi harga saham adalah metode Autoregressive Moving Average (ARIMA). Untuk meningkatkan akurasi dari prediksi harga saham, akan diimplementasikan Genetic Algorithm (GA) pada model ARIMA terbaik yang didapatkan dari proses ARIMA. Hasil dari penelitian ini menunjukkan bahwa prediksi harga saham dengan menggunakan model ARIMA (1,1,1) memiliki nilai Root Mean Square Error (RMSE) sebesar 418.1314. Sedangkan hasil prediksi harga saham dengan mengimplementasikan GA pada model ARIMA (1,1,1) dengan 600 generasi, 1200 generasi, 1800 generasi, 2400 generasi, dan 3000 generasi masing-masing memiliki nilai RMSE berturut-turut sebesar 5827.738, 1319.903, 1080.704, 563.7984, dan 371.0107. Hasil yang didapat menunjukkan bahwa pengimplementasian GA pada ARIMA dengan 3000 generasi dapat meningkatkan akurasi prediksi harga saham, yaitu dengan memiliki nilai RMSE sebesar 371.0107.Kata Kunci: GA, Harga Saham, Model ARIMA, Prediksi, RMSE
Implementasi Dempster Shafer dalam Pembentukkan Portofolio Mean Varian Muhammad Iqbal Cholil; Deni Saepudin; Aniq Atiqi Rohmawati
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 1 (2020): Maret, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.1.371

Abstract

Implementasi Dempster Shafer dalam pembentukkan portofolio saham Mean Varian menghasilkan nilai performansi saham yang menjadi acuan untuk pemilihan saham kedalam portofolio saham Mean Varian. Nilai performansi saham dihasilkan dari data variansi return dan faktor fundamental saham Indeks LQ45 yang dihitung menggunakan aturan kombinasi Dempster Shafer. Saham dengan nilai performansi tertinggi dipilih kedalam portofolio saham Mean Varian. Pada penelitian ini, terdapat 10 saham yang dipilih ke dalam portofolio saham yaitu BSDE, GGRM, INDF, SGRO, SMGR, SCMA, MNCN, BBCA, HMSP, dan BMTR dengan menghasilkan portofolio return sebesar 0,0125. Evaluasi kinerja portofolio diterapkan dengan menggunakan metode Sharpe Ratio dengan hasil yang didapat portofolio saham dengan metode Dempster Shafer sebesar 0,2063 dan portofolio saham Mean Varian tanpa Dempster Shafer sebesar 0,0905. Hasil dari penelitian ini, portofolio saham Mean Varian dengan metode Dempster Shafer memiliki kinerja portofolio yang lebih baik dibandingkan portofolio saham Mean Varian tanpa metode Dempster Shafer. Kata Kunci: Dempster Shafer, Portofolio Return, Portofolio Mean Varian, Sharpe Ratio
Forecasting Number of New Cases Daily COVID-19 in Central Java Province Using Exponential Smoothing Holt-Winters Dinda Fitri Irandi; Aniq Atiqi Rohmawati; Putu Harry Gunawan
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.565

Abstract

There is hard to mention how long the COVID-19 pandemic will discontinue. There are some factors, including the public’s efforts to slow spread and researchers’ work to observe more about this outbreak. From the beginning of the health crisis, particularly following the announcement of the first positive case In Indonesia due to the COVID-19 on March 2, 2020. Afterwards, the number of daily cases increase simultaneously in other regions in Indonesia until today. Due to the fact that the significant mobility of the people, Central Java has contributed the 3rd rank of potential number of COVID-19 positive cases in Indonesia. This study aims to forecast the number of COVID-19 daily new cases in Central Java to assist the government in preparing the necessary resources and controlling the spread of the COVID-19 virus in Central Java Province. We proposed Exponential Smoothing Holt-Winters with the Additive model with seasonal addition considering trend and seasonal factors. The dataset during March 14 to April 17, 2021, revealed fluctuation of trend and seasonal patterns. Our simulation studies indicate that Exponential Smoothing Holt-Winters provides sharp and well performance for forecasting daily new cases of COVID-19 in Central Java province with MAPE less than 10%.
An Exponential Smoothing Holt-Winters Based-Approach for Estimating Extreme Values of Covid-19 Cases Abi Rafdhi Hernandy; Aniq Atiqi Rohmawati; Putu Harry Gunawan
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.576

Abstract

Covid-19 is an ongoing outbreak across the world infecting millions, having significant fatality rate, and triggering economic disruption on a large scale. The demand of healthcare facility has been significantly affected by the increased Covid-19 cases. Many countries have been forced to do lockdown and physical distancing to avoid a crucial peak of novel Covid-19 pandemic that potentially overwhelms healthcare services. Central Java is the province with the third highest population density in Indonesia and predicted to be affected significantly over a particular period of this outbreak. Our paper aims to provide a modelling to estimate extreme values of daily Covid-19 cases in Central Java, between March and April 2021. We particularly capture seasonality during this period using Exponential Smoothing Holt-Winters. We employ that Value at Risk and mean excess function based-approaches for extreme value estimation. Our simulation studies indicate that Exponential Smoothing Holt-Winters and Value at Risk provide sharp and well prediction for extreme value with zero violation. Since a number of positive cases has resulted unprecedented volatility, estimating the extreme value of daily Covid-19 cases become a crucial matter to support maintain essential health services.
PELATIHAN VISUALISASI DAN ANALISIS DATA MENGGUNAKAN TABLEAU DI SMKN 3 BANDUNG Jondri Jondri; Aniq Atiqi Rohmawati
Charity : Jurnal Pengabdian Masyarakat Vol 4 No 2 (2021): Charity-Jurnal Pengabdian Masyarakat
Publisher : PPM Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/charity.v4i2.3435

Abstract

Menurut data Dinas Tenaga Kerja (Disnaker) Kota Bandung, penyumbang terbesar pengangguran dikota Bandung adalah lulusan SMK. Salah satu penyebabnya adalah tidak sesuainya kompetensi lulusan SMK dengan kebutuhan pasar tenaga kerja. Salah satu jenis pekerjaan yang sedang booming saat ini adalah Big Data Analis, khususnya dibidang e-comerce. Perusahaan membutuhkan tenaga yang dapat mengambil knowledge dari data penjualan untuk dijadikan dasar bagi pihak manajemen untuk mengambil keputusan. SMK Negeri 3 Bandung adalah sekolah kejuruan di Bidang Bisnis Manajemen, Pariwisata, dan Teknik Informatika yang membuka 5 jurusan Kompetensi Keahlian yang semuanya sudah terakreditasi A. Jurusan di SMK Negeri 3 Bandung sangat cocok bagi generasi milenial yang dituntut siap menghadapi persaingan bisnis era kekinian. Disetiap 5 jurusan Kompetensi Keahlian yang terdapat di SMKN 3 Bandung mengajarkan komputer pada peserta didiknya. Selain itu SMKN 3 bandung mempunyai 12 buah ruang praktek komputer. Hal ini merupakan modal dasar yang kuat bagi guru-guru SMKN 3 Bandung mempelajari Big Data analisis, khususnya tentang visualisasi data yang berukuran besar. Pengabdian masyarakat ini dilaksanakan dalam 2 periode, yaitu peiode 1 dan 2 tahun 2020. Pada pengabdian masyarakat periode 1 diberikan dasar-dasar visualisasi data dengan Tableu. Pada periode 2 diberikan materi visualisasi data yang lebih kompleks dan beberapa metode analisis data dengan menggunakan Tableu
Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying Public Opinion about COVID-19 Vaccine in Indonesia Siti Saadah; Kaenova Mahendra Auditama; Ananda Affan Fattahila; Fendi Irfan Amorokhman; Annisa Aditsania; Aniq Atiqi Rohmawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (557.978 KB) | DOI: 10.29207/resti.v6i4.4215

Abstract

COVID-19 was classified as a pandemic in March 2020, and then in July 2021, this virus had its variance that spreads all over the world including Indonesia. The probability of the detrimental of its effect cannot be avoided, because this virus has a huge transmission risk during daily activity. To prevent suffering from COVID-19, people certainly need to be vaccinated. In responding to its vaccine, the citizen of Indonesia become expressive, so they try to express opinions, for example by uploading text on Twitter. Those expressions can be learned using deep learning frameworks which are BERT, CNN-LSTM, and IndoBERTweet to get knowledge about negative speech categories such as anxiety, panic, and emotion, or positive speech such as vaccines whether worked well. By then, these three methods accomplish in carrying out the prediction of sentiments about vaccination using dataset tweets on Twitter from January-2021 to March-2022, for instance using IndoBERT succeeds to classify sentiments as positive sentiment at around 80%, and then IndoBERTweet at 68%, in addition using CNN-LSTM reach 53% with the total of using 2020 dataset from Twitter. According to these results, a lesson learned for continued improvement for Indonesia's Government or authorities can be acquired in ending the COVID-19 pandemic.
Prediction of Bandung City Traffic Classification Using Machine Learning and Spatial Analysis Adhitya Aldira Hardy; Aniq Atiqi Rohmawati; Sri Suryani Prasetyowati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4538

Abstract

This research proposes a visualization of Bandung City congestion map classification using machine learning and kriging interpolation methods. The machine learning methods used are Naive Bayes and Artificial Neural Network (ANN) for the congestion classification process. The kriging interpolation used is simple kriging to create a spatial location map visualization on the congestion classification prediction. They are based on the classification results of both methods. Naïve Bayes is ideal supervised learning for classification, while ANN is ideal unsupervised learning for prediction. The classification was performed on arterial and collector roads with 11 intersections that are congestion points. The data used is traffic counting data for Bandung City in April 2022. The congestion classification is divided into four categories based on the congestion level. This category division causes data imbalance, so the Random Oversampling technique is used to overcome data imbalance. The result is that the ANN method has better performance, with an accuracy rate of 93% and an RMSE value of 0.9746, while the Naïve Bayes method has an accuracy rate of 90% and an RMSE value of 0.9381. The resulting classification map shows that in April 2022, the southern area of Bandung City experienced the highest congestion compared to the northern, western and southern areas. This research provides the best algorithm between the two methods. It provides information on congestion in Bandung City by visualizing the congestion classification map to reduce traffic congestion in the city of Bandung.
Boosting Methods For Dengue Incidence Rate Prediction in Bandung District Fhira Nhita; Didit Adytia; Aniq Atiqi Rohmawati
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 13 No 3 (2022): Vol. 13, No. 3 December 2022
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2022.v13.i03.p05

Abstract

Dengue infections are among the top 10 diseases that cause the most deaths worldwide. Dengue is a severe global threat and problem, especially in tropical countries like Indonesia. The Indonesian Ministry of Health also stated that dengue is as dangerous as COVID-19. One of the preventive actions that can be taken is by controlling vectors (the Aedes aegypti mosquito) where weather factors influence their breeding. In this study, the prediction of dengue incidence rate is carried out using three boosting methods i.e., Extreme Gradient Boosting, Adaptive Boosting, and Gradient Boosting. The data used are monthly data of dengue incidence rate and weather data. The case study used is Bandung district, West Java Province, Indonesia. The important issues that is investigated in this study is to find the weather parameters that have the most influence on IR and gradually improve the prediction model through three test scenarios. From the test results, the weather parameter that has the most influence on the next month's IR is temperature. Meanwhile, the best training data length is five years (2016-2020). Finally, the best prediction model achieved by AdaBoost method with value of Root Mean Square Error and Correlation Coefficient for testing data (January-December 2021) are 0.55 and 0.95, respectively.
House Prices Segmentation Using Gaussian Mixture Model-Based Clustering Muhammad Hafidh Raditya; Indwiarti; Aniq Atiqi Rohmawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4459

Abstract

House is a place for humans to live and the main necessity for humans. For years, the need for houses is increasing and varied so it affects the selling price of the house. Therefore, more research is needed to learn about the selling price of houses. This research is only focusing on house price segmentation in DKI Jakarta using the Gaussian Mixture Model-Based Clustering Method with the Expectation-Maximization algorithm. The goal of this research is to make a house price segmentation model so that we can obtain useful information for the potential buyer. Clustering with GMM utilizes the log-likelihood function to optimize the GMM parameters. The result of this research is housed in DKI Jakarta and can be segmented into 3 different clusters. The first cluster is for the low-profile houses. The second cluster is for the mid-profile houses. The third cluster is for high-profile houses. The silhouette score that was produced by the clustering method is 0.60866 meaning that this score is quite good because it’s close to a value of 1.
Peramalan Return Portofolio Saham - Saham Lq45 Menggunakan Metode Weighted Fuzzy Time Series Muhamad Lutfi Chandra; Rian Febrian Umbara; Aniq Atiqi Rohmawati
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstrak Return portofolio merupakan return investasi dalam berbagai instrumen keuangan selama suatu periode tertentu. Salah satu jenis portofolio adalah equal weight portofolio. Metode yang efektif untuk mendapatkan nilai peramalan return portofolio saham – saham LQ45 adalah metode weighted fuzzy time series. Saham-saham LQ45 adalah saham-saham yang dikelompokan terhadap 45 saham yang memiliki tingkat likuiditas perdagangan di atas rata-rata tingkat likuiditas saham lainnya yang terdaftar di Bursa Efek Indonesia. Saham yang digunakan dalam membentuk portofolio equal weight yaitu 20 saham dengan rata – rata return terbesar. Hasil peramalan return portofolio menggunakan metode weighted fuzzy time series sangat efektif dibandingkan dengan fuzzy time series dengan RMSE (Root Mean Squared Error) WFTS 0.03109848 sedangkan tingkat error menggunakan fuzzy time series (FTS) yaitu 0.047415. Kata kunci : LQ45, Portofolio, return, Weighted fuzzy time series (WFTS), Root Mean Squared Error (RMSE), Fuzzy Time Series (FTS). Abstract Portfolio return is a return on investment in a variety of financial instruments during a certain period. One type of portfolio is equal weight portfolio. An effective method for obtaining stock return portfolio forecasting values - LQ45 shares is a weighted fuzzy time series method. LQ45 shares are shares that are grouped against 45 shares that have a level of trading liquidity above the average level of liquidity of other shares listed on the Indonesia Stock Exchange. The shares used in forming the equal weight portfolio are 20 shares with the highest average return. The results of portfolio return forecasting using the weighted fuzzy time series method is very effective compared to fuzzy time series with RMSE (Root Mean Squared Error) WFTS 0.03109848 while the error rate uses fuzzy time series (FTS) that is 0.047415. Keywords: LQ45, Portfolio, return, Weighted f
Co-Authors A. Maulana Mukhsin Abdurrazaq Naufal Abi Rafdhi Hernandy Abi Rafdhi Hernandy Adhitya Aldira Hardy Adiwijaya Agri Pratomo Alfian Yudha Iswara Ananda Affan Fattahila Annisa Aditsania Arifin Dwi Kandar Saputro Arnasli Yahya Ayu Wulandari Bagas Yafitra Pandji Bambang Eko Supriyadi Benedikto Krisnandy Wijaya Budi Ihsan Daulay Cipta Rahmadayanti Danar Satrio Aji Dara Ayu Lestari Deni Saepudin Dian Tiara Didit Adytia Dinda Fitri Irandi Dini Apriliani Lestari Ditta Febriany Sutrisna Elvina Oktavia Ergon Rizky Perdana Purba Erick Anugrah Prihananta Farah Diba Febry Triyadi Fendi Irfan Amorokhman Fhira Nhita Fikri Nur Hadiansyah Fiqi Ruli Setiawan Fitriaini Amalia Gharyni Nurkhair Mulyono Hadyatma Dahna Marta Hasbi Rabbani I Komang Gede Rusmawan Ihsan Hasanudin Ilma Mufidah Imannda Kusuma Putra Indwiarti Irandi, Dinda Fitri Irfan Fauzan Prasetyo Irwan Ramadhana Jeshurun Eliezer Cussoy Jondri Jondri Justinus Dedy Handyka Simanjuntak Kaenova Mahendra Auditama Kautsar Abdillah Lani Rohaeni Laode Muhammad Ali Al-Qomar Lola Yolanda Ruth Herinis Lumbanraja Mailia Putri Utamil Muhamad Lutfi Chandra Muhammad Akmal Afghani Muhammad Fadhil Maulana Muhammad Hafidh Raditya Muhammad Iqbal Cholil Muhammad Irfan Fathurrahman Nanda Putri Mintari Nathan Sukmawan Ni Luh Ketut Dwi Murniati Nisrina Nur Faizah Novelya Nababan Nur Nining Aulia Putu Harry Gunawan Rabbani, Hasbi Rahmadayanti, Cipta Rahmattullah, Rizky Raisa Betha Meiliza Rangga Arya Pamungkas Redha Arifan Juanda Reima Agustina Kusumawardani Reiza Krisnaviardi Reza Pratama Rian Febrian Umbara Rimba Whidiana Ciptasari Rizki Ayudiah Kartika Paramita Rizky Pujianto Rizky Retno Utami Rizma Nurviarelda Sabilla Fitriyantini Shuni’atul Ma’wa Siti Saadah Sri Suryani Prasetiyowati Suhendar, Annisya Hayati Susy Sundari Syahrizal Rizkiana Rusamsi Syaifrijal Zirkon Radion Tasya Salsabila Tedo Hariscandra Triandini Nurislamiaty Yahya, Arnasli ZulvanFirdaus ZulvanFirdausImanullah