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All Journal Media Statistika JURNAL SISTEM INFORMASI BISNIS Telematika : Jurnal Informatika dan Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) Jurnal Sistem informasi dan informatika (SIMIKA) 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 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 Joong-Ki
Hindrayani, Kartika Maulida
Sains Data, Universitas Pembangunan Nasional Veteran Jawa Timur
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 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
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.
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.
ANALISIS SENTIMEN KEPUASAN PELAYANAN TRANSPORTASI ONLINE GOJEK MENGGUNAKAN ALGORITMA EXTREME LEARNING MACHINE Riskiyah, Ameliyah; Fahrudin, Tresna Maulana; Hindrayani, Kartika Maulida
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 2 (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.v5i2.714

Abstract

With the rapid advancement of technology, online transportation has become the main solution for many people in Indonesia to travel easily and efficiently. Companies such as GOJEK are constantly innovating to improve their services, resulting in many responses and reviews from users. This research aims to analyze customer satisfaction with these online transportation services by analyzing the sentiment of user opinions on the Twitter platform. Sentiment analysis plays a very important role in decision making by classifying user reviews. Data was retrieved through a crawling process using specific keywords related to each service. The data preprocessing process includes case folding, tokenizing, normalization, stemming, filtering, and convert negation. This aims to clean and prepare the data so that it can be processed using the algorithm better. This process includes removing irrelevant elements from the text data, converting the text into a consistent or more standardized form, reducing the number of features in the data by stemming, and converting the text into numbers or vectors so that it can be processed by the algorithm. Feature extraction is performed using the Word2Vec model to convert text into a numerical vector representation that can later be processed by ELM. Converts words into numeric vectors in a high-dimensional space, where words that have the same context in the text are close to each other in that space. The ELM (Extreme Learning Machine) algorithm is used as a classification model due to its high training speed and good generalization ability. Model evaluation is done using confusion matrix which measures classification performance through accuracy, precision, recall matrix. The results of this study show that the ELM algorithm with Word2Vec feature extraction is able to classify user sentiment with a high level of accuracy. This research provides insight into user satisfaction with online transportation services and can be a reference for companies to improve their service quality
Indonesian Sign Language (BISINDO) Classification Using Xception Transfer Learning Architecture Amelia, Meisya Vira; Saputra, Wahyu Syaifullah Jauharis; Hindrayani, Kartika Maulida; Riyantoko, Prismahardi Aji
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1392

Abstract

Human communication generally relied on speech. However, this was not applicable to the deaf people, who depended on sign language for daily interactions. Unfortunately, not everyone had the ability to understand sign language. In higher education environments, the lack of individuals proficient in sign language often created inequality in the learning process for deaf students. This limitation could be addressed by fostering a more inclusive environment, one of which was through the implementation of a sign language translation system. Therefore, this study aimed to develop a machine learning model capable of detecting and translating Indonesian Sign Language (BISINDO) alphabet gestures. The model was built using the Xception transfer learning method from Convolutional Neural Networks (CNN). The dataset consisted of 26 BISINDO alphabet gestures with a total of 650 images. The model was evaluated using K-Fold cross-validation and achieved an F1-score of 94% during testing.
Perancangan Aplikasi EMKASADA untuk Penjadwalan Kegiatan Perkuliahan Program Studi Sains Data UPN Veteran Jawa Timur Pakpahan, Vera Febrianti; Afidria, Zulfa Febi; Bhalqis, Anissa Andiar; Hindrayani, Kartika Maulida; Trimono
Journal of Technology and Informatics (JoTI) Vol. 7 No. 1 (2025): Vol. 7 No.1 (2025)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v7i1.835

Abstract

The development of information technology has encouraged innovation in various fields, including education. Lecture scheduling is one important aspect that requires special attention to ensure efficient and effective use of resources. The EMKASADA application improves efficiency in lecture scheduling by automating the process of preparing schedules, thus reducing the time and manual effort in managing schedules. With features such as dashboards, lecturer data, courses, days, sessions, rooms, lecturers, and automatic scheduling, this system is able to speed up the schedule preparation process and optimize the allocation of available resources. In terms of effectiveness, the EMKASADA application ensures that scheduling is more optimal by minimizing the possibility of clashes between lecturer schedules, courses, and rooms. With the waterfall method approach, the system is developed in a structured and systematic manner, following the stages from requirements analysis to maintenance. Testing was conducted using the black box testing method to ensure all application features, such as dashboards, lecturer data, courses, days, sessions, rooms, lecturers, and scheduling, function properly. The test results show that the features in the EMKASADA application function properly and are able to increase efficiency in scheduling lectures.
Exploratory Data Analysis and Machine Learning Algorithms to Classifying Stroke Disease Riyantoko, Prismahardi Aji; Fahrudin, Tresna Maulana; Hindrayani, Kartika Maulida; Idhom, Mohammad
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (517.79 KB) | DOI: 10.33005/ijconsist.v2i02.49

Abstract

This paper presents data stroke disease that combine exploratory data analysis and machine learning algorithms. Using exploratory data analysis we can found the patterns, anomaly, give assumptions using statistical and graphical method. Otherwise, machine learning algorithm can classify the dataset using model, and we can compare many model. EDA have showed the result if the age of patient was attacked stroke disease between 25 into 62 years old. Machine learning algorithm have showed the highest are Logistic Regression and Stochastic Gradient Descent around 94,61%. Overall, the model of machine learning can provide the best performed and accuracy.
Co-Authors Afidria, Zulfa Febi Ahmad, Davin Anezta Alfiansyah , Achmad Dzulfiqar Altetiko, Faizal Johan Alzam, Muhammad Arsyad Amelia, Meisya Vira Azizah, Alisa Jihan Bhalqis, Anissa Andiar Budi, Aditya Septa Christina, Enzelica Vica Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Elmaliyasari, Shifa Eristya Maya Safitri Fahrudin, Tresna Maulana Fajar Ramadhani Fernando, Moch. Firman Fitriani, Aulia Nur idhom, Mohammad Isyanto, Aisyah Kirana Putri Made Hanindia Prami Swari Maulana F, Tresna Ningrum, Imelda Widya Nur Aini Rakhmawati Pakpahan, Vera Febrianti Pratiwi, Nanda Aulia Prismahardi Aji Riyantoko Prismahardi Aji Riyantoko Putro, R. Kokoh H. rachmanto, Nugroho Fajar Riskiyah, Ameliyah Riyantoko, Prismahardi Aji Safira, Alya Mirza Saputra, Wahyu S. J. Saputra, Wahyu Syaifullah Jauharis Tresna Maulana Fahruddin Tresna Maulana Fahrudin Trimono Trimono, Trimono Wara, Shindi Shella May Wardati, Nanda Kurnia Wibowo, Muhammad Bagas Satrio