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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Simetris PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal technoscientia Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Informatika dan Rekayasa Perangkat Lunak JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Pendidikan dan Teknologi Indonesia Journal of Dinda : Data Science, Information Technology, and Data Analytics COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Journal of Computers and Digital Business Journal of Intelligent Software Systems Journal of Public Health Concerns Eduvest - Journal of Universal Studies Journal Of Artificial Intelligence And Software Engineering CYBER-Techn(Jurnal Informatika dan Industri) INOVTEK Polbeng - Seri Informatika BHAKTI JIVANA Quality
Andriyani, Widyastuti
Universitas Teknologi Digital Indonesia
Religion Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Other

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Mushroom Image Classification Using C4.5 Algorithm Cucut Hariz Pratomo; Widyastuti Andriyani
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.930

Abstract

This study applied five types of Mushrooms, they are Button mushrooms, Wood Ear mushrooms, Straw mushrooms, Reishi mushrooms and Red Oyster mushrooms. The feature extraction used is Order 1 with the parameters of mean, skewness, variance, kurtosis, and entropy. The process carried out to identify mushroom images by preparing image objects. There were 15 images of each mushroom class were taken for each mushroom and stored in .jpg format. The image processing is carried out by a feature extraction process. Then five images for each mushroom class are chosen. They were used as test images which will be classified so that identification results are obtained. This study applies the Classification Algorithm C4.5 to build a decision tree, which will also identify the results of the accuracy of processed mushroom images. The obtained result of accuracy was 84% in the classification of feature extraction Order 1
IOT BASED SOIL MOISTURE MONITORING AND SOIL MOISTURE PREDICTION USING LINEAR REGRESSION (CASE STUDY OF VINCA PLANTS) Kuindra Iriyanta; Bambang Purnomosidi Dwi Putranto; Widyastuti Andriyani
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.929

Abstract

Soil moisture is something that becomes important. Indonesia as an agricultural country, most of the population has a profession as a farmer. In agriculture, one of the important parts is the water composition in the soil or soil moisture. One attempt to maintain soil moisture is to provide sufficient water intake to the soil. However, in practice, it is sometimes complicated for farmers to do proper irrigation of their agricultural land. This humidity condition will ultimately determine the success of vinca plant cultivators. The accuracy of giving water both in terms of time management and volume are two things which are an important focus of vinca crop growing. This system is designed using a humidity sensor which is used to measure the moisture composition contained in the soil, and an air temperature sensor. The NodeMCU ESP2866 microcontroller acts as a link between Google spreadsheet sensors. NodeMCU ESP2866 will send humidity and temperature sensor reading data to Google spreadsheets using a RESTfull API which can connect one application to another. The sensor data is then saved to Google spreadsheet and processed using the linear regression method. The processing results will be displayed on the Google Data Studio dashboard. The output of this process is to provide information about soil moisture conditions, notification of soil moisture conditions if it is too dry or damp, thus the prevention of the death of vinca plants can be carried out. The benefit for users is that they can carry out periodic and real-time monitoring by simply using the Telegram instant messaging application, which is expected to reduce the risk of plant death due to drought or excessive watering
Clustering Analysis of Poverty Levels in North Sumatra Province Using the Application of Data Mining with the K-Means Algorithm Widyastuti Andriyani; Asyahri Hadi Nasyuha; Yohanni Syahra; Bagas Triaji
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6867

Abstract

North Sumatra, as one of the largest provinces in Indonesia, has serious challenges related to poverty that require serious attention North Sumatra, as one of the largest provinces in Indonesia, has serious challenges related to poverty that require serious attention and in-depth analysis. Thus, research on poverty levels in this province becomes very relevant and urgent. Therefore, a more in-depth analysis is needed regarding poverty levels in various regions within this province using data mining methods. The data mining approach is a way to gain understanding from large amounts of data. In the context of the problem of poverty levels, data mining has the potential to help reveal patterns that may be hidden in economic and social data. One algorithm that is often applied in clustering analysis is the K-Means algorithm. The K-Means algorithm is a clustering method that is widely used in data analysis and allows grouping data based on similar characteristics, so that it can be used to classify areas with similar levels of poverty. The results of this research show that data mining with the application of the K-Means algorithm can help produce more effective decisions in analyzing clustering of poverty levels in North Sumatra Province involving the use of data over a ten-year period, namely from 2013 to 2022, which records the number of poor people based on District and city. 3 groups were produced, namely 3 levels of poverty, including relatively stable, very vulnerable and vulnerable. Data from 33 districts or cities in North Sumatra resulted in a poverty level clustering of 1 city that was very vulnerable, 4 cities that were vulnerable and 27 cities that were relatively stable.
Maintaining Query Performance through Table Rebuilding & Archiving Andriyani, Widyastuti; Pujianto, Pujianto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 1 (2024): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90062

Abstract

Despite the system previously utilizing optimal query configurations and database settings, the transaction table in the database, which is undergoing significant numerical increases and notable queries and updates on each line, has seen a drop in query speeds simultaneous with data growth. This situation arises due to an increase in disk space in the database tablespace, which results from block fragmentation. At times, database engines do not detect this problem, thereby overlooking it in the database recommendation engine. Lacking an understanding of the fundamental issue, database engineers need analysis and strategies to maintain the query speed of the transaction table in the relational database
Gaussian Blur Filter Effect Analysis on Facial Detection Accuracy Using Viola Jones Method Saryanto, Saryanto; Andriyani, Widyastuti
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 3 (2024): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.96017

Abstract

Human face detection is one of the most studied topics in computer vision. The purpose of facial detection is to find out whether or not a face is present in an image. Blur can be caused by many things, such as motion that occurs when the camera takes a picture or the use of a camera that is not focused when taking a picture. For facial recognition, blur becomes difficult to get information about an object, get a description about it, or identify a face in the image. The more blur a picture, the more difficult it is to identify it. This research applies the Viola-Jones relative method for facial detection with a high degree of accuracy and fast computation. This study analyzed the influence of a gaussian blur filter by calculating how much radius an object has been given a gausian blur filter so that it can no longer be identified as an object, and also looking for the minimum PSNR value that is still acceptable in the object detection process. The minimum PSNR value for the image is 16.6 dB, and the minimum PSR value before the face can no longer be detected is 17.84 dB.
Penerapan Metode Profile Matching Dalam Penilaian Kinerja dan Perangkingan Prestasi Guru Ismail, Taufik; Andriyani, Widyastuti
Jurnal Pendidikan dan Teknologi Indonesia Vol 4 No 12 (2024): JPTI - Desember 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.455

Abstract

Dalam upaya untuk meningkatkan kualitas pendidikan, sebuah instansi pendidikan perlu melakukan penilaian yang tepat terhadap kinerja dan prestasi guru. SMK Pancasila 1 Kutoarjo merupakan lembaga pendidikan yang berkomitmen untuk terus memperbaiki mutu pendidikan bagi siswa-siswanya. Dalam proses penilaian kinerja guru, SMK Pancasila 1 Kutoarjo saat ini masih menggunakan sistem manual. Sistem ini memiliki beberapa kelemahan, seperti waktu yang dibutuhkan untuk mengumpulkan dan menganalisis data yang cukup lama, serta potensi kesalahan manusia yang dapat memengaruhi hasil penilaian. Oleh karena itu, diperlukan sebuah Decision Support System (DSS) yang dapat membantu dalam proses penilaian dan perangkingan prestasi guru secara lebih objektif dan efisien. Dalam penelitian ini, metode yang digunakan untuk melakukan penilaian kinerja dan menentukan peringkat guru adalah Profile Matching. Metode ini memungkinkan penilaian yang lebih sistematis dan terukur. Hasil dari penelitian ini menunjukkan bahwa guru dengan kinerja terbaik adalah Kandidat 4. Dengan penerapan sistem ini, diharapkan penilaian kinerja guru dapat dilakukan dengan lebih akurat, sehingga kualitas pendidikan di SMK Pancasila 1 Kutoarjo dapat terus ditingkatkan, dan pada akhirnya berdampak positif bagi perkembangan siswa.
Sentiment Analysis of X Platform on Viral 'Fufufafa' Account Issue in Indonesia Using SVM Suryanto, Suryanto; Andriyani, Widyastuti
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.104158

Abstract

In this study, we conducted a comprehensive sentiment analysis of users on the social media platform X concerning the viral controversy surrounding the KasKus account known as “Fufufafa.” This issue attracted widespread attention and sparked varied reactions within the online community. To gain insights into public opinion on the topic, we utilized the Support Vector Machine (SVM) method, a widely recognized machine learning algorithm for classification tasks. The data for this research was gathered from various posts, comments, and public discussions on platform X, which were pre-processed to filter out irrelevant information, such as spam, unrelated topics, and non-informative content. After cleaning the data, user sentiments were categorized into three primary classes: positive, negative, and neutral. The SVM model was then trained and tested using a labeled dataset to accurately predict user sentiments based on the textual content of their interactions.
Pengujian Perangkat Lunak E-Commerce : Perbandingan Antara Pengujian Manual dan Otomatis dengan Katalon Studio dan Python Brian Duen Rakly; Widyastuti Andriyani
COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat Vol. 4 No. 8 (2024): COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/comserva.v4i8.2737

Abstract

Pengujian perangkat lunak merupakan elemen krusial dalam siklus pengembangan perangkat lunak untuk memastikan kualitas, keandalan, dan keamanan sistem. Pengujian manual melibatkan keterlibatan manusia secara langsung untuk mengevaluasi perangkat lunak berdasarkan skenario tertentu, sedangkan pengujian otomatis memanfaatkan perangkat lunak untuk menjalankan pengujian secara otomatis sesuai skenario yang telah ditentukan. Penelitian ini bertujuan membandingkan dua metode pengujian perangkat lunak, yaitu pengujian manual dan pengujian otomatis dengan menggunakan alat seperti Katalon Studio dan Python. Pengujian manual lebih efektif dalam kasus pengujian yang memerlukan penilaian manusia dan fleksibilitas terhadap perubahan kondisi yang tidak terduga, seperti evaluasi pengalaman pengguna (UX). Di sisi lain, pengujian otomatis lebih efisien untuk pengujian rutin dan berulang, sehingga mengurangi potensi kesalahan manusia dan mempercepat siklus pengujian. Studi ini mengevaluasi efektivitas, efisiensi, serta biaya yang terkait dengan kedua metode tersebut dalam konteks pengujian perangkat lunak e-commerce. Dengan memanfaatkan alat-alat seperti Katalon Studio dan bahasa pemrograman Python, penelitian ini menganalisis keunggulan dan keterbatasan dari setiap pendekatan, serta memberikan wawasan mengenai situasi di mana pengujian manual atau otomatis lebih tepat digunakan. Hasil dari penelitian ini diharapkan dapat memberikan panduan bagi pengembang perangkat lunak dalam memilih metode pengujian yang sesuai berdasarkan kebutuhan spesifik proyek pengembangan perangkat lunak mereka
Comprehensive Lakehouse Data Architecture Model for College Accreditation Nenen Isnaeni; Bambang Purnomosidi Dwi Putranto; Widyastuti Andriyani; Siti Khomsah
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i1.1759

Abstract

Accreditation is an assessment activity that determines the feasibility of study programs at a university. College accreditation data comes from various sources and includes multiple data types: semi-structured, unstructured, or structured. Over time, the volume of data will continue to grow and develop, so there is a possibility of data redundancy and a long time to collect the data needed for accreditation activities. The solution is integrating data. This research aims to design a data architecture to facilitate the management of university accreditation data using the Lakehouse data architecture model. All data types can be stored on one platform in the Lakehouse data architecture. In this research, the identification, integration, and data transformation process for university accreditation data is carried out. The data used in this research is academic data in which there are with. The study's results provide an overview of the data flow process in the Lakehouse data architecture model to help better manage university accreditation data. This architecture also supports real-time data analysis so that the accreditation process can be carried out more effectively and efficiently. Keywords: accreditation, data analysis, data architecture, data lakehouse, data warehouse
Analisis Sentimen pada Ulasan Produk dengan SVM dan Word2Vec ANDRIYANI, WIDYASTUTI; Astuti, Yuli; Wisesa, Bradika Almandin; Hengki, Hengki
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 1 (2025): Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i1.1498

Abstract

Analisis sentimen adalah salah satu cabang pemrosesan bahasa alami (NLP) yang bertujuan untuk mengidentifikasi opini dalam teks. Penelitian ini mengusulkan model analisis sentimen dengan menggunakan kombinasi Word2Vec sebagai teknik representasi fitur dan Support Vector Machine (SVM) sebagai algoritma klasifikasi. Dataset yang digunakan adalah Amazon Customer Reviews, dengan 500 ribu sampel ulasan produk yang dilabeli sebagai sentimen positif atau negatif. Model yang diusulkan dibandingkan dengan baseline seperti Naive Bayes dan Logistic Regression, yang menggunakan representasi fitur berbasis TF-IDF.Hasil evaluasi menunjukkan bahwa SVM dengan Word2Vec menghasilkan akurasi 91.3\%, precision 90.8\%, recall 92.1\%, dan F1-score 91.4\%, lebih unggul dibandingkan model baseline. Grafik Precision-Recall Curve dan ROC Curve memperkuat temuan bahwa Word2Vec memberikan representasi fitur yang lebih informatif, yang secara signifikan meningkatkan performa SVM dalam tugas klasifikasi teks.Penelitian ini membuktikan efektivitas kombinasi Word2Vec dan SVM untuk analisis sentimen pada dataset besar dan kompleks. Pendekatan ini relevan untuk berbagai domain, seperti e-commerce dan analisis opini di media sosial, serta membuka peluang untuk pengembangan lebih lanjut menggunakan model berbasis transformer.
Co-Authors Akhmad Dahlan Andre Argisitawan Anwarudin Anwarudin Arif Setiadi, Rizki Arma Fauzi Asyahri Hadi Nasyuha B.T. Sutrisno Bagas Triaji Bambang P.D.P Bambang Purnomosidi Dwi Putranto Bradika Almandin Wisesa Brahmana, Ivanna Beru Brian Duen Rakly Cucut Hariz Pratomo D P, Bambang Purnomosidi Danny Kriestanto, Danny Dian Tri Wiyanti Dommy Kristomo Domy Kristomo, Domy Duen Rakly, Brian Dwi Wibowo Eny Retna Ambarwati Faizal Makhrus Faizal Makhrus Femi Dwi Astuti Femi Dwi Astuti Fika Pratiwi Firman Noor Hasan Hamdani Hamdani Hendra Hengki Hengki Heri Muhrial Herwantono, Herwantono Hizkia Hendra Rianingsih Istichomah Istichomah Ivónia Fátima Ruas da Silva Kuindra Iriyanta Laksono, Triyan Agung Miftahul Huda MILASARI, LISA ASTRIA Muhammad Ali Sofian Murgi Handari Nenen Isnaeni Nugroho, Daniel C.A. Nugroho, Muhammad Agung Nurohman, Muhamad P.D.P., Bambang Pangestika , Elza Qorina Pereira, Elisabet da Conceição Prisilia Talakua Pujianto Pujianto Purnomosidi D.P, Bambang Purnomosidi Dwi Putranto, Bambang Purnomosidi, Bambang Putra, Fadhlih Girindra Rajie Al Qadri Anwar Rakly, Brian Duen Reni Tri Lestari Retantyo Wardoyo Riadinata Riadinata Rifky Lana Rahardian Rikie Kartadie Robertus Saptoto Roh Bintang Jaya, Mabrur Ruas da silva, Ivonia Fatima Said, Famidin Saputra, Andika Jodhi Saryanto Saryanto Sipayung, Hotma Sadariahta Siti Khomsah, Siti Sri Redjeki Suningrat, Nining Suryanto Suryanto Taufik Ismail Totok Suprawoto Tri Andi, Tri Wibowo, Gunturari Wijayanti, Agnes Erida Wiwi Widayani, Wiwi Yohanni Syahra Yuli Astuti