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All Journal Media Statistika JURNAL SISTEM INFORMASI BISNIS Telematika : Jurnal Informatika dan Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) G-Tech : Jurnal Teknologi Terapan International Journal of Advances in Data and Information Systems ESTIMASI: Journal of Statistics and Its Application Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Journal of Technology and Informatics (JoTI) International Journal of Data Science, Engineering, and Analytics (IJDASEA) International Journal Of Computer, Network Security and Information System (IJCONSIST) Journal of International Conference Proceedings Journal of Data Mining and Information Systems Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal PETISI (Pendidikan Teknologi Informasi) Joong-Ki
Kartika Maulida Hindrayani
Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology

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Penentuan Prioritas Pengambilan Pesanan Barang Oleh Angkutan Kota dengan Metode Rule-Based System Rakhmawati, Nur Aini; Budi, Aditya Septa; Altetiko, Faizal Johan; Ramadhani, Fajar; Wardati, Nanda Kurnia; Hindrayani, Kartika Maulida
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 2 (2018): Volume 8 Nomor 2 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (341.764 KB) | DOI: 10.21456/vol8iss2pp195-202

Abstract

Angkotin is a system that provides an alternative for urban transport to not only be used for passenger transportation, but also as freight service. Therefore, it needs a decision support system for taking order to delivery to the destination according to each criterion from urban transportation. The method used to develop this decision support system is a rule-based system. The result of this research is a decision support system that can help public transportation to find orders that can be taken based on four factors, such as distance, direction, route code, and status of storage capacity. Based on these four factors, the system can provide an order recommendation under the appropriate conditions through the Angkotin application. Based on our experiment, our system performs on 7 seven cases as expected.   
Cluster Analysis of Hospital Inpatient Service Efficiency Based on BOR, BTO, TOI, AvLOS Indicators using Agglomerative Hierarchical Clustering Tresna Maulana Fahrudin; Prismahardi Aji Riyantoko; Kartika Maulida Hindrayani; Made Hanindia Prami Swari
Telematika Vol 18, No 2 (2021): Edisi Juni 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i2.4786

Abstract

Purpose: The research proposed an approach for grouping hospital inpatient service efficiency that have the same characteristics into certain clusters based on BOR, BTO, TOI, and AvLOS indicators using Agglomerative Hierarchical Clustering.Design/methodology/approach: Applying Agglomerative Hierarchical Clustering with dissimilarity measures such as single linkage, complete linkage, average linkage, and ward linkage.Findings/result: The experiment result has shown that ward linkage was given a quite good score of silhouette coefficient reached 0.4454 for the evaluation of cluster quality. The cluster formed using ward linkage was more proportional than the other dissimilarity measures. Ward linkage has generated cluster 0 consists of 23 members, cluster 1 consists of 34 members, while both of cluster 2 and 3 consists of only 1 member respectively. The experiment reported that each cluster had problems with inpatient indicators that were not ideal and even exceeded the ideal limit, but cluster 0 generated the ideal BOR and TOI parameters, both reached 52.17% (12 of 23 hospital inpatient) and 78.36% (18 of 23 hospital inpatient) respectively.Originality/value/state of the art: Based on previous research, this study provides an alternative to produce more proportional, representative and quality clusters in mapping hospital inpatient service efficiency that have the same characteristics into certain clusters using Agglomerative Hierarchical Clustering Method compared to the K-means Clustering Method which is often trapped in local optima. 
ANALISIS PREDIKSI HARGA SAHAM SEKTOR PERBANKAN MENGGUNAKAN ALGORITMA LONG-SHORT TERMS MEMORY (LSTM) Prismahardi Aji Riyantoko; Tresna Maulana Fahruddin; Kartika Maulida Hindrayani; Eristya Maya Safitri
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 1 (2020): Peran Digital Society dalam Pemulihan Pasca Pandemi
Publisher : Jurusan Teknik Informatika

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

Abstract

AbstractInvesting, buying or selling activity on the stock exchange requires knowledge and skill in the field of data analysis. The movement of the curve in the stock market place is very dynamic, hence it requires data modelling to predict stock prices in order to get a price with a high degree of accuracy. Currently, machine learning has a good level of accuracy in processing and predicting data. In this work, we proposed the data modelling using the Long-Short Term Memory (LSTM) algorithm to predict stock prices. The main purpose for this research is to analyze the accuracy of the machine learning algorithm in predicting stock price data and analyzing the number of epochs in the optimal model formation. The results of our study indicate that the LSTM algorithm has an accurate level of prediction as indicated by the RMSE value and the data model obtained the variation of the epochs value.Keywords : LSTM Algorithm, Stock Price, Analysis Prediction, Machine LearningUntuk melakukan investasi atau jual beli di bursa saham memerlukan pemahaman dibidang analisis data. Pergerakan kurva pada pasar saham sangat dinamis, sehingga memerlukan pemodelan data untuk melakukan prediksi harga saham agar mendapatkan harga dengan tingkat akurasi yang tinggi. Machine Learning pada saat ini memiliki tingkat keakuratan yang baik dalam mengolah dan memprediksi data. Pada penelitian ini kami melakukan pemodelan data menggunakan algoritma Long-Short Term Memory (LSTM) untuk memprediksi harga saham. Tujuan utama pada jurnal ini adalah untuk menganalisis tingkat keakuratan algoritma Machine Learning dalam melakukan prediksi data harga saham serta melakukan analisis pada banyaknya epochs dalam pembentukan model yang optimal. Hasil penelitian kami menunjukkan bahwa algoritma LSTM memiliki tingkat prediksi yangg akurat dengan ditunjukkan pada nilai RMSE serta model data yang di dapatkan pada variasi nilai epochs.Kata Kunci : Algoritma LSTM, Harga Saham, Analisis Prediksi, Machine Learning
Water Availability Forecasting Using Univariate and Multivariate Prophet Time Series Model for ACEA (European Automobile Manufacturers Association) Prismahardi Aji Riyantoko; Tresna Maulana Fahrudin; Kartika Maulida Hindrayani; Amri Muhaimin; Trimono
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 2 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1292.381 KB) | DOI: 10.33005/ijdasea.v1i2.12

Abstract

Time series is one of method to forecasting the data. The ACEA company has competition with opened the data in the Water Availability and uses the data to forecast. The dataset namely, Aquifers-Petrignano in Italy in water resources field has five parameters e.g. rainfall, temperature, depth to groundwater, drainage volume, and river hydrometry. In our research will be forecast the depth to groundwater data using univariate and multivariate approach of time series using Prophet Method. Prophet method is one of library which develop by Facebook team. We also use the other approach to making the data clean, or the data ready to forecast. We use handle missing data, transforming, differencing, decomposition time series, determine lag, stationary approach, and Augmented Dickey-Fuller (ADF). The all approach will be uses to make sure that the data not appearing the problem while we tried to forecast. In the other describe, we already get the results using univariate and multivariate Prophet method. The multivariate approach has presented the value of MAE 0.82 and RMSE 0.99, it’s better than while we forecast using univariate Prophet.
Metric Comparison For Text Classification Amri Muhaimin; Tresna Maulana Fahrudin; Trimono; Prismahardi Aji Riyantoko; Kartika Maulida Hindrayani
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 1 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 1,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i1.34

Abstract

Text classifications have been popular in recent years. To classify the text, the first step that needs to be done is to convert the text into some value. Some values that can be used, such as Term Frequencies, Inverse Document Frequencies, Term Frequencies – Inverse Document Frequencies, and Frequency of the word itself. This study aims to get which metric value is best in text classification. The method used is Naïve Bayes, Logistic Regression, and Random Forest. The evaluation score that is used is accuracy and Area Under Curve value. It comes out that some metric values produce similar evaluation scores. Another finding is that Random Forest is the best method among others, also the best metric for text classification is Term Frequencies – Inverse Document Frequencies.
Implementation of Web Scraping on Google Search Engine for Text Collection Into Structured 2D List Tresna Maulana Fahrudin; Prismahardi Aji Riyantoko; Kartika Maulida Hindrayani
Telematika Vol 20, No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.9575

Abstract

Purpose: This research proposes the implementation of web scraping on Google Search Engine to collect text into a structured 2D list.Design/methodology/approach: Implementing two important stages in the process of collecting data through web scraping, namely the HTML parsing process to extract links (URL) on Google Search Engine pages, and HTML parsing process to extract the body text from website pages on each link that has been collected.Findings/result: The inputted query is adjusted to the latest issues and news in Indonesia, for example the President's important figures, the month of Ramadan and Idul Fitri, riots tragedy (stadium) and natural disasters, rising prices of basic commodities, oil and gold, as well as other news. The least number of links obtained was 56 links and the most was 151 links, while the processing time to obtain links for each of the fastest queries was 1 minute 6.3 seconds and the longest was 2 minutes 49.1 seconds. The results of scraping links from these queries were obtained from Wikipedia, Detik, Kompas, the Election Supervisory Body (Bawaslu), CNN Indonesia, the General Election Commission (KPU), Pikiran Rakyat, and others.Originality/value/state of the art: Based on previous research, this study provides an alternative to produce optimal collection of links and text from web scraping results in the form of a 2D list structure. Lists in the Python programming language can store character sequences in the form of strings and can be accessed using index keys, and manipulate text efficiently.
ANALISIS SENTIMEN TERHADAP ISU FEMINISME DI TWITTER MENGGUNAKAN MODEL CONVOLUTIONAL NEURAL NETWORK (CNN) Brescia Ayundina Yuniarossy; Kartika Maulida Hindrayani; Aviolla Terza Damaliana
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i1.585

Abstract

The development of technology is very significant in various fields, especially in the field of digital technology. Sentiment analysis of feminism issues on Twitter tends to be significant in understanding public opinion, especially Twitter users. Being a place for people to vent, Twitter spreads the message of those who tweet to a wide audience and it often happens that a tweet becomes an influence on public opinion. Twitter can be a tool to find out public sentiment towards a figure, group, and organization. Feminism is a movement to voice the rights of a human being to be equal regardless of gender. In this study, a Convolutional Neural Network (CNN) approach is used to analyze sentiment towards the issue of feminism on Twitter. The data collected from Twitter contains a variety of conversations, opinions, and views on feminism. By building and training a CNN model that is able to process text data and classify sentiment based on each tweet. By applying the CNN model, it aims to identify sentiment patterns towards Twitter users on the issue of feminism, especially the topics of domestic violence and sexual harassment. Where these two topics will be discussed in this research. Another goal is to provide valuable insights for researchers, activists, and policy makers in understanding the dynamics of public opinion on the issue of domestic violence and sexual harassment. The results of this sentiment analysis are expected to make a significant contribution to supporting discussions on social issues on social media
Daily Forecasting for Antam's Certified Gold Bullion Prices in 2018-2020 using Polynomial Regression and Double Exponential Smoothing Fahrudin, Tresna Maulana; Riyantoko, Prismahardi Aji; Hindrayani, Kartika Maulida; Diyasa, I Gede Susrama Mas
Journal of International Conference Proceedings Vol 3, No 4 (2020): Proceedings of the 8th International Conference of Project Management (ICPM) Mal
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jicp.v3i4.1009

Abstract

Gold investment is currently a trend in society, especially the millennial generation. Gold investment for the younger generation is an advantage for the future. Gold bullion is often used as a promising investment, on other hand, the digital gold is available which it is stored online on the gold trading platform. However, any investment certainly has risks, and the price of gold bullion fluctuates from day to day. People who invest in gold hopes to benefit from the initial purchase price even if they must wait up to five years. The problem is how they can notice the best time to sell and buy gold. Therefore, this research proposes a forecasting approach based on time series data and the selling of gold bullion prices per gram in Indonesia. The experiment reported that Holt’s double exponential smoothing provided better forecasting performance than polynomial regression. Holt’s double exponential smoothing reached the minimum of Mean Absolute Percentage Error (MAPE) 0.056% in the training set, 0.047% in one-step testing, and 0.898% in multi-step testing.
Development of Brand Awareness Through Social Media Marketing of UMKM Fried Chicken in Medokan Ayu Surabaya Hindrayani, Kartika Maulida; Maulana F, Tresna; Ningrum, Imelda Widya; Isyanto, Aisyah Kirana Putri
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4139

Abstract

The development of information technology has many benefits for partner actors to make processes automatic in increasing productivity and marketing. Marketing management in today's technological world requires a strategy for disseminating information and expanding marketing targets. Skills in using social media as a digital marketing tool can increase consumers or customers' ability to recognize and remember a product being promoted. This will also increase brand awareness. The method used is a development method with observation steps in the field, identifying partner’s problems and weaknesses, offering solutions to partners, designing training materials, implementing training material designs and integrating materials. The results of the development of brand awareness using social media, we use Instagram Platform and Google Review. Hopefully this will raise awareness of the UMKM Fried Chicken with its franchise located in Medokan Ayu. Good relations, complete explanations and clear communication with partners will support marketing development through brand awareness through social media.
Penerapan Cross Validation sebagai Analisis Sentimen Pelayanan Publik Kereta Api Lokal Daop 8 Menggunakan Metode Multinomial Naïve Bayes Risnaldy Novendra Irawan; Kartika Maulida Hindrayani; Mohammad Idhom
G-Tech: Jurnal Teknologi Terapan Vol 8 No 2 (2024): G-Tech, Vol. 8 No. 2 April 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i2.4117

Abstract

Dalam menyediakan layanan yang efisien dan berkualitas untuk Masyarakat pada sektor transportasi kereta api wilayah daerah operasional 8 Surabaya, perlu dilakukan langkah untuk memenuhi harapan pengguna kereta api subsidi. Penelitian ini bertujuan untuk mengetahui pengaruh cross validation terhadap metode Multinomial Naïve Bayes dalam analisis sentimen menggunakan metode 10-fold cross validation. Langkah-langkah preprocessing data dilakukan sehingga didapatkan data  sebanyak 1123 komentar dengan dua kelas yaitu kelas positif sebanyak 778 komentar dan negatif sebanyak 345 komentar. Analisis dilakukan sebanyak 2 kali dengan proses validasi 10-fold cross validation dan pengujian Multinomial Naïve Bayes. Berdasarkan hasil pengujian Multinomial Naïve Bayes menggunakan data uji sebanyak 225 data, didapatkan 14 data positif dan 160 data negatif yang ditinjau dari performa pengujian terbaik pada parameter nilai validasi fold = 1. Hasil akhir didapatkan nilai akurasi 77%, presisi 81%, recall 77%, dan f1 score 71%, yang mengungkapkan bahwa model efektif dalam mengklasifikasi komentar negatif dari kesluruhan data uji.
Co-Authors Adhigiadany, Chelsea Ayu Afidria, Zulfa Febi Ahmad, Davin Anezta Aisyah Kirana Putri Isyanto Alfiansyah , Achmad Dzulfiqar Altetiko, Faizal Johan Alzam, Muhammad Arsyad Amelia, Meisya Vira Amri Muhaimin Aviolla Terza Damaliana Azizah, Alisa Jihan Betty Dewi Puspasari Bhalqis, Anissa Andiar Brescia Ayundina Yuniarossy Budi, Aditya Septa Burhan Syarif Acarya Christina, Enzelica Vica Damaliana, Aviolla Terza Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Elmaliyasari, Shifa Eristya Maya Safitri Fahrudin, Tresna Maulana Fajar Ramadhani Fernando, Moch. Firman idhom, Mohammad Imelda Widya Ningrum Isyanto, Aisyah Kirana Putri Kartini Kartini Made Hanindia Prami Swari Maulana F, Tresna Mohammad Idhom Ningrum, Imelda Widya Nur Aini Rakhmawati Pakpahan, Vera Febrianti Pratiwi, Nanda Aulia Prismahardi Aji Riyantoko Purwadwika, Reza Sadiya Putri, Milla Akbarany Baktiar Putro, R. Kokoh H. rachmanto, Nugroho Fajar Riskiyah, Ameliyah Risnaldy Novendra Irawan Riyantoko, Prismahardi Aji Rizkiyah, Selly Rizqin, Indira Zein Safira, Alya Mirza Saputra, Wahyu S. J. Saputra, Wahyu Syaifullah Jauharis Shindi Shella May Wara Tresna Maulana F Tresna Maulana Fahruddin Tresna Maulana Fahrudin Tresna Maulana Fahrudin Trimono Trimono, Trimono Wardati, Nanda Kurnia Wibowo, Muhammad Bagas Satrio