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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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Identifikasi Penyakit Daun Jeruk Siam Menggunakan Convolutional Neural Network (CNN) dengan Arsitektur EfficientNet Burhan Syarif Acarya; Amri Muhaimin; Kartika Maulida Hindrayani
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.v8i2.4120

Abstract

Jeruk siam menjadi salah satu komoditas hortikultura yang memegang peranan utama dalam sektor pertanian Indonesia dengan jumlah produksi yang mencapai 2 juta ton setiap tahunnya. Namun, produksi jeruk siam rentan terhadap serangan hama dan penyakit, terutama pada bagian daun. Penyakit yang umum terjadi termasuk Blackspot Leaf, Canker Leaf, Greening Leaf, Powdery Mildew, dan Citrus Leafminer. Pada umunya identifikasi penyakit pada tanaman jeruk dilakukan secara manual sehingga penentuan penyakit cenderung subyektif. Oleh karena itu, diperlukan solusi otomatis dalam mendeteksi penyakit pada daun jeruk. Tujuan penelitian yaitu untuk mengidentifikasi penyakit yang menyerang daun jeruk menggunakan metode deep learning yaitu CNN dengan arsitektur EfficientNetB3. Dataset yang digunakan adalah citra penyakit daun jeruk yang diambil langsung dari kebun jeruk yang dibagi menjadi 6 kelas seperti pada penyakit yang disebutkan di atas. Hasil penelitian menggunakan skenario epoch 10 dengan optimizer Adam memperoleh hasil akurasi terbaik yaitu 0,98 (98%).
Development of Brand Awareness Through Social Media Marketing of UMKM Fried Chicken in Medokan Ayu Surabaya Kartika Maulida Hindrayani; Tresna Maulana F; Imelda Widya Ningrum; Aisyah Kirana Putri Isyanto
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.
Identifying Academic Excellence: Fuzzy Subtractive Clustering of Student Learning Outcomes Wibowo, Muhammad Bagas Satrio; Hindrayani, Kartika Maulida; Trimono, Trimono
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4614

Abstract

Education forms a vital foundation for a nation's future. In this digital era, while the use of Information and Communication Technology (ICT) in education is increasing, it brings increasingly complex challenges in education data management and analysis. The growing number of students each year results in a large volume of data, which would be difficult to manage if still relying on manual methods. Manual approaches are inefficient, time-consuming, prone to inconsistencies and human error, especially when identifying outstanding students in large and complex data. This research aims to implement a clustering system to group outstanding students at XYZ elementary school using the Fuzzy Subtractive Clustering (FSC) method. FSC was chosen for its ability to identify data groups based on the density of data points. FSC involves several important parameters, including radius, squash factor, acceptance ratio, and rejection ratio. Added variabel of social and spiritual values aims to enhance grouping quality by offering a broader perspective on students' character, attitudes, and social interactions. Parameter exploration shows an increase in the silhouette score from 0.20–0.45 to 0.45-0.57 and variable addition spiritual and social values, which indicates clearer cluster separation and provides better insights. The best parameters results were achieved with radius 0.3, accept ratio 0.5, reject ratio 0.04, and squash factor 1.25, resulting in a Silhouette Score of 0.57 and forming 5 student groups. Cluster results can guide special mentoring for students with low academic, spiritual, and social values, and support personalized learning programs based on each cluster’s characteristics.
Analisis Sentimen Terhadap Ulasan Aplikasi Mobile JKN Menggunakan Metode Machine Learning Logistic Regression, SVM, dan CSVM Fernando, Moch. Firman; Ahmad, Davin Anezta; Rachmanto, Nugroho Fajar; Wara, Shindi Shella May; Hindrayani, Kartika Maulida
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.44943

Abstract

One of the digital-based public service innovations in the health sector is the Mobile JKN application developed by BPJS Kesehatan. This application allows people to get health services more easily, effectively, and integrated. The purpose of this study is to evaluate user perceptions of the Mobile JKN application through collecting reviews from the Google Play Store. The collected data was analyzed using TF-IDF text mining technique and Chi-Square feature selection. Furthermore, logistic regression, support vector machine (SVM), and clustered SVM (CSVM) algorithms were used to perform sentiment classification. Comments were categorized into three categories: positive, neutral, and negative. The evaluation results show that CSVM has an accuracy value of 93%, precision of 94%, recall of 84%, and F1 value of 89%. Although features such as online registration and digital cards received positive feedback, sentiment analysis showed that most reviews were negative, especially regarding technical issues. The results show that ML-based algorithms can be effectively used to assess how people perceive digital services. These results can be used as a basis for BPJS Kesehatan to improve and develop new services.
Implementation of Transfer Function ARIMA Model for Stock Price Prediction Azizah, Alisa Jihan; Prasetya, Dwi Arman; Hindrayani, Kartika Maulida; Fahrudin, Tresna Maulana
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.v6i2.1396

Abstract

Dynamic economic growth requires stable financing sources, one of which is through the capital market. In stock investment activities, risk and return are two fundamental aspects that are interrelated and must be carefully considered. The volatility of ASII stock prices, influenced by various factors including exchange rates, can create uncertainty in investment decision-making. This study aims to predict the stock price of PT Astra International Tbk (ASII) using a transfer function model approach that integrates the influence of the Indonesian rupiah to US dollar exchange rate as an external variable. The transfer function model is an extension of the ARIMA model that can measure the dynamic relationship between input and output variables. Based on the estimation results, the best model obtained has a transfer function order of (b,s,r) = (1,0,0) with a noise series of (p_n,q_n) = (1,0). The prediction results show that ASII stock price movements tend to be stable with a gradual decline over the next 20 days. Model evaluation demonstrates low error rates, with MAE of 84.19, RMSE of 110.37, and MAPE of 1.65%. These results indicate that the transfer function model is effective in modeling and predicting short-term stock prices with reasonably good accuracy.
Deteksi Sentimen Komentar Aplikasi Gobis Suroboyo dengan Metode Naive Bayes dan Metode Regresi Logistik Elmaliyasari, Shifa; Alzam, Muhammad Arsyad; Pratiwi, Nanda Aulia; Wara, Shindi Shella May; Hindrayani, Kartika Maulida
JDMIS: Journal of Data Mining and Information Systems Vol. 3 No. 2 (2025): August 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v3i2.4691

Abstract

This research discusses sentiment analysis of user comments on the Gobis Suroboyo application using the Naive Bayes algorithm and Logistic Regression. Data was obtained through web scraping method from Google Play Store, with a total of 1,015 comments which then went through text pre-processing such as data cleaning, case folding, stemming, normalisation, filtering, tokenizing, and feature selection using TF-IDF. Sentiment labels were determined based on user ratings, with ratings above 3 as positive and 3 and below as negative. The results show that the Naive Bayes algorithm is better at classifying positive sentiment with a precision of 81% and f1-score of 77%, while Logistic Regression excels at negative sentiment with a precision of 82% and f1-score of 82%. The WordCloud visualisation shows dominant words such as “app”, “good”, and “bus stop” that reflect users attention to the app features and transportation services. The findings show that both algorithms have competitive and reliable performance for evaluating public opinion on comment-based digital services. This research is expected to be a reference for app developers and local governments in improving the quality of digital public services.
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