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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Rekayasa Hijau MIND (Multimedia Artificial Intelligent Networking Database) Journal REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Journal Social Science And Technology For Community Service Journal of Data Science and Information System
FAHRUDIN, NUR FITRIANTI
Program Studi Sistem Informasi - Institut Teknologi Nasional Bandung
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering

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Penerapan Algoritma Apriori untuk Market Basket Analysis FAHRUDIN, NUR FITRIANTI
MIND Journal Vol 4, No 1 (2019): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (443.725 KB) | DOI: 10.26760/mindjournal.v4i1.13-23

Abstract

Pemanfaatan teknik data mining saat ini dapat membantu para pemilik bisnis untukmeningkatkan penjualan produk mereka. Salah satu teknik yang sangat dikenaladalah analisis asosiasi. Analisis asosiasi bertujuan untuk menemukan hubunganantara barang-barang yang dibeli oleh pelanggan. Analisis asosiasi semacam ini biasadikenal sebagai market basket analysis. Market basket analysis menggunakan datapelanggan yang selama ini disimpan didalam basis untuk menemukan informasi barudidalamnya. Algoritma Apriori merupakan algoritma untuk melakukan market basketanalisis, yang bertujuan untuk menemukan barang yang paling sering dibeli.Algoritma Apriori ini menghasilkan sebuah aturan asosiasi yang bermanfaat bagipelaku bisnis. Untuk memilih aturan asosasi yang paling kuat diperlukan perhitunganlift ratio. Dengan menghitung lift ratio dari setiap aturan asosiasi, dapat diketahuiaturan asosiasi yang valid dan paling kuat. Dengan melakukan analisis asosiasi,dapat diketahui bahwa data pelanggan dapat dimanfaatkan sebagai masukan kepadapemilik bisnis untuk menentukan strategi penjualan bagi bisnis mereka.
PROSES MINING UNTUK OPTIMASI PROSES BISNIS Nur Fitrianti Fahrudin
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 6 No. 2 (2020)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (541.671 KB) | DOI: 10.33197/jitter.vol6.iss2.2020.365

Abstract

Organizations currently need to conduct an analysis of their business processes in order to improve business performance and productivity. In addition, this analysis can be a way to compete with competitors. However, the analysis of this business process if done manually requires considerable time. Process mining is a technique that helps solve this problem. Information systems that are owned by a company certainly store their every business activity. This data can be processed to find business processes that occur. This data is usually called an event log. Event logs help organizations to find gaps between business processes that occur with those expected. Based on this gap business processes can later be evaluated for later improvement.
Training on the Introduction of TOEFL (Test of English as Foreign Language) to High School Students in Bandung Levita Dwinaya; Corry Caromawati; Nur Fitrianti Fahrudin; Sofia Umaroh; Arni Sukmiarni
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 3, No 2 (2022): REKA ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v3i2.106-115

Abstract

The Test of English as a Foreign Language (TOEFL) is a type of language test of which results are often used as a reference for new student admission in Universities. However, this test is not included in the English language learning curriculum at the secondary level so high school students are not familiar with it. Therefore, this community service activity aims at introducing the test to middle-level students so that they have knowledge and are familiar with the types of questions tested in this test. To determine the progress of students' knowledge and abilities, pre and post-tests were conducted, and the result was the improvement of the scores indicating that the learning approach chosen in this activity is in accordance with the objective of the training. In addition, a simple information system for test assessment was created using Microsoft Excel. This information system helps record student grades and makes it easier to convert TOEFL scores.
Penerapan Algoritma Apriori untuk Market Basket Analysis NUR FITRIANTI FAHRUDIN
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 4, No 1 (2019): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v4i1.13-23

Abstract

ABSTRAKPemanfaatan teknik data mining saat ini dapat membantu para pemilik bisnis untuk meningkatkan penjualan produk mereka. Salah satu teknik yang sangat dikenal adalah analisis asosiasi. Analisis asosiasi bertujuan untuk menemukan hubungan antara barang-barang yang dibeli oleh pelanggan. Analisis asosiasi semacam ini biasa dikenal sebagai market basket analysis. Market basket analysis menggunakan data pelanggan yang selama ini disimpan didalam basis untuk menemukan informasi baru didalamnya. Algoritma Apriori merupakan algoritma untuk melakukan market basket analisis, yang bertujuan untuk menemukan barang yang paling sering dibeli. Algoritma Apriori ini menghasilkan sebuah aturan asosiasi yang bermanfaat bagi pelaku bisnis. Untuk memilih aturan asosasi yang paling kuat diperlukan perhitungan lift ratio. Dengan menghitung lift ratio dari setiap aturan asosiasi, dapat diketahui aturan asosiasi yang valid dan paling kuat. Dengan melakukan analisis asosiasi, dapat diketahui bahwa data pelanggan dapat dimanfaatkan sebagai masukan kepada pemilik bisnis untuk menentukan strategi penjualan bagi bisnis mereka.Kata kunci: Algoritma Apriori, Market Basket Analysis, AsosisasiABSTRACTData mining techniques today can help business owners to increase sales of theirproducts. One well-known technique is association analysis. Association analysis aims to find relationships between items purchased by customers. This kind of association analysis is commonly known as market basket analysis. Market basket analysis uses customer data that has been stored on the base to find new information in it. A priori algorithm is an algorithm for doing market basketball analysis, which aims to find the items that are most often purchased. This a priori algorithm produces an association rule that is beneficial for business people. To choose the strongest association rule, it is necessary to calculate the elevator ratio. By calculating the elevator ratio of each association rule, you can find the valid and strongest association rules. By conducting association analysis, it can be seen that customer data can be used as input for business owners to determine sales strategies for their business.Keywords: Apriori Algorithm, Market Basket Analysis, Association
Sequence Clustering in Process Mining for Business Process Analysis Using K-Means NUR FITRIANTI FAHRUDIN
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 6, No 1 (2021): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v6i1.16-30

Abstract

ABSTRAKProses Discovery merupakan teknik utama dalam proses mining yang bertujuan untuk menghasilkan sebuah model dari event log. Namun dalam implementasinya ditemukan masalah, karena banyak varian proses yang terdapat pada event log. Hal ini membuat hasil proses discovery sulit untuk dipahami. Penelitian ini di awali dengan mengelompokan event log menggunakan metode K-Means sebagai tahap pre-processing. Hasil dari tahap pre-processing ini kemudian di modelkan menggunakan teknik proses mining. Namun, pada saat metode K-Means ini di terapkan penentuan jumlah cluster yang optimal sangatlah penting. Kesalahan dalam menentukan nilai K dapat menurunkan nilai fitness dan precision dari model yang dihasilkan. Berdasarkan hasil pengujian pada data set issue tracking dengan jumlah case 1091 dan jumlah event 7924  yang terbagi ke dalam empat cluster nilai precision meningkat dari 0,49 menjadi 1 dan nilai fitness meningkat dari 0,34 menjadi kisaran 0,61-1 pada cluster 2, 3 dan 4. Kata kunci: K-Means, proses mining, event log, clustering, sequence clusteringABSTRACTProcess Discovery as the main technique in the mining process aims to produce a model of an event log. However, in the implementation, there is a problem found, for a lot of process variants contained in the event log. This makes the results of the discovery process difficult to understand. This research begins by grouping event logs using the K-Means method as a pre-processing stage. The results of this pre-processing stage are then modeled using the process mining technique. However, determining the optimal number of clusters is crucial. Mistakes in determining the K value can reduce the fitness value and precision of the resulting model. Based on the test results on the issue tracking data set with the number of cases 1091 and the number of events 7924 which is divided into four clusters the precision value increased from 0.49 to 1 and the fitness value increased from 0.34 to 0.61-1 in clusters 2, 3 and 4.Keywords: K-Means, process mining, event log, clustering, sequence clustering  
Algoritma Scale Invariant Feature Transform (SIFT) pada Deteksi Kendaraan Bermotor di Jalan Raya YUSUP MIFTAHUDDIN; NUR FITRIANTI FAHRUDIN; MOCHAMAD FACHRY PRAYOGA
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 5, No 1 (2020): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v5i1.54-65

Abstract

AbstrakProses perhitungan jumlah kendaraan yang masih dilakukan secara manual dan membutuhkan banyak operator dalam pendataan. Berdasarkan hal itu, diperlukan sistem yang mampu mendeteksi dan mengklasifikasi kendaraan yang melintas di jalan raya secara otomatis. Dalam mengidentifikasi citra kendaraan, sistem menggunakan algoritma SIFT. Hasil fitur akan dibandingkan dengan metode K-Nearest Neighbor (KNN). Sistem dibangun untuk mendeteksi jenis kendaraan berat dengan mengukur tingkat akurasi keberhasilan berdasarkan nilai pencahayaan, jumlah objek, perubahan rotasi, serta pada kondisi siang dan malam hari. Dataset yang digunakan berjumlah 100 citra kendaraan berat. Kinerja sistem pada kondisi siang hari mendapat nilai presisi rata-rata 100%, nilai recall 54%, dan nilai akurasi 78%. Hasil pengukuran presisi dan recall, diperoleh nilai f-measure sebesar 67 %.Kata kunci: SIFT, kendaraan berat, K-Nearest NeighbourAbstractThe process of collecting vehicles still done manually and requires a lot of human resources. Therefore, we need a system that can detect and classify vehicles passing on the highway automatically. SIFT is an algorithm for identification of an image. The features will be compared using the K-Nearest Neighbor (KNN) method. In this study,  system will be designed to detect the type of heavy vehicle using the SIFT method to measure the accuracy of success based on the value of lighting, number of objects, changes in rotation, and day night conditions. Dataset used was 100 heavy vehicle images. The system performance during daytime conditions gets an average precision value of 100%, a recall value of 54%, and an accuracy value of 78%. From the results of precision and recall, the f-measure value is 67 %.Keywords: SIFT, heavy vehicles, K-Nearest Neighbour 
Modeling Inventory Systems Using The User Experience Design Model Method Nur Fitrianti Fahrudin; Agung Deni Wahyudi
Journal of Data Science and Information Systems Vol. 1 No. 1 (2023): Volume 1 Number 1 February 2023
Publisher : Journal of Data Science and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/dimis.v1i1.12

Abstract

Information systems are the process of collecting and processing transaction data and communicating data into information for decision-making. User Experience Design (UXD) is the process of developing a product to increase user satisfaction with a product by increasing usability, accessibility, and satisfaction provided in the interaction with a product or application. The main factor in the success of application developers depends on the user experience that users feel, therefore it is very important to make this a priority in developing designs. This method is able to make your design look neat, simple, intuitive, flexible, and attractive as well as provide a different experience to users of your product or service and make you look unique in front of other competitors. Modeling the inventory system uses a user centered design model with five stages, namely empethized, define problem statements, indentation, and prototype. The results of this study are in accordance with the needs of users in creating a web-based inventory information system that can overcome the problems faced by the school. The results of testing the inventory application using the blackbox testing model which has a value of 100% in accordance with testing the functionality of the system, and the test results using web quality 4.0 obtained a total score of 91.53%. Based on this, the Web Quality 4.0 test results have very good criteria.
Influence of Data Scaling and Train/Test Split Ratios on LightGBM Efficacy for Obesity Rate Prediction FAHRUDIN, NUR FITRIANTI; PUTRA, KURNIA RAMADHAN; UMAROH, SOFIA; LAUTAN, GAMAS BLOORY
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 9, No 2 (2024): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v9i2.220-234

Abstract

AbstrakNormalisasi adalah proses yang tidak dapat dilewatkan dalam data mining yang membantu menyesuaikan nilai atribut data ke skala yang sama. Dalam konteks data mining, perbedaan skala antar atribut dapat menyebabkan kesalahan dalam pemodelan atau interpretasi hasil. Penggunaan normalisasi dalam pra-pemrosesan masih diperdebatkan, terutama ketika menggunakan algoritma dari kelompok pohon keputusan.  Penelitian ini membandingkan model dengan data yang dinormalisasi dan tidak dinormalisasi dengan menggunakan metode normalisasi, MinMaxScaler, MaxAbsScaler, dan RobustScaler. Hasil penelitian menunjukkan bahwa model LightGBM tanpa normalisasi memiliki tingkat akurasi sebesar 96,6 dalam mengklasifikasikan tingkat obesitas pada data saat ini. Tidak hanya normalisasi yang mempengaruhi hasil klasifikasi, tetapi juga jumlah rasio antara data pelatihan dan pengujian. Penelitian menunjukkan bahwa semakin besar persentase data yang digunakan untuk pelatihan, semakin tinggi tingkat akurasinya. Pada dataset obesitas, rasio 80:20 memiliki akurasi hingga 97%.Kata kunci: Decision Tree, LightGBM, Obesitas, Data Mining, KlasifikasiAbstractNormalization is an essential process in data mining that helps adjust the values of data attributes to the same scale. In data mining, differences in attribute scales can lead to errors in modeling or interpreting results. Normalization in preprocessing is still debated, particularly when using algorithms from the decision tree family. This study compares models with normalized and non-normalized data using normalization methods such as MinMaxScaler, MaxAbsScaler, and RobustScaler. The results show that the LightGBM model without normalization achieved an accuracy rate of 96.6% in classifying obesity levels in the current dataset. Not only does normalization affect classification results, but the ratio between training and testing data also plays a role. The study indicates that the larger the percentage of data used for training, the higher the accuracy rate. In the obesity dataset, an 80:20 ratio resulted in an accuracy rate of up to 97%.Keywords: Decision Tree, LightGBM, Obesity, Data Mining, Classification
Segmentation-Based Fractal Texture Analysis (SFTA) to Detect Mass in Mammogram Images DEWI, IRMA AMELIA; FAHRUDIN, NUR FITRIANTI; RAINA, JODI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 1: Published January 2021
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i1.203

Abstract

ABSTRAKDi Indonesia, kasus kanker paling banyak adalah kanker payudara yaitu 58.256 kasus atau 16,7% dari total 348.809 kasus kanker. Dibutuhkan suatu sistem yang dapat membantu pakar untuk mendeteksi kanker payudara pada wanita dengan mengindentifikasi citra mammogram. Keabnormalan dapat dideteksi dari massa pada mammogram yaitu area dengan pola tekstur dan bentuk serta batas tertentu. Berdasarkan hal tersebut maka dibuat sebuah sistem yang dapat mendeteksi massa kanker pada citra mammogram menggunakan Segmentation-Based Fractal Texture Analysis (SFTA). Tahapan pertama akuisisi citra, dilanjut dengan segmentasi menggunakan k-means dan thresholding. Hasil dari segmentasi citra dilakukan tahapan morfologi menggunakan opening dan masking. Setelah itu dilakukan ekstraksi fitur SFTA, dan klasifikasi Support Vector Machine (SVM). Hasil pengujian penelitian ini didapatkan nilai akurasi sebesar 90%, presisi sebesar 87,75%, recall sebesar 93,33%dan f1-score 90,32% dengan nilai number of threshold (nt) SFTA adalah 3Kata kunci: mammogram, SFTA, kanker payudara, klasifikasi ABSTRACTIn Indonesia, the most cancer cases were breast cancer, namely 58,256 cases or 16.7% of the total 348,809 cancer cases. A system is required to assist the expert in detecting breast cancer in women by identifying mammogram images. Abnormalities in a mammogram are determined in part of texture with a particular form and specific limit, usually called a ‘mass.’ Image acquisition is perceived as the first step, followed by segmentation using the k-means and the thresholding. Image segmentation undergoes the morphological analysis steps using opening and masking methods, after feature extraction processing by SFTA, using Support Vector Machine (SVM) for classification processing. The obtained research result revealed an accuracy value of 90%, a precision value of 87.75%, a recall value of 93.33%, and an F1-Score of 90.32%, with the number of thresholds (nt) of SFTA amounting to 3.Keywords: Breast cancer, Mammogram, Classification, SFTA
Optimasi Bundling Produk Toko Roti berbasis Waktu menggunakan Algoritma FP-Growth Fahrudin, Nur Fitrianti; Maulana, Rifki; Barmawi, Mira Musrini
Rekayasa Hijau : Jurnal Teknologi Ramah Lingkungan Vol 8, No 3 (2024)
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/jrh.v8i3.297-308

Abstract

AbstrakPenelitian ini bertujuan untuk mengeksplorasi penggunaan teknik asosiasi untuk mengidentifikasi pola tersembunyi dalam data transaksi penjualan. Analisis pola pembelian pelanggan  yang terkandung dalam sebuah data transaksi dilakukan pada dataset milik sebuah Toko Roti. Dataset memiliki atribut utama seperti nomor transaksi, item, kemudian variabel waktu  seperti daytime, daypart dan daytype.  Guna memaksimalkan hasil rekomendasi, dataset dibagi kedalam 6 bagian berdasarkan waktu pembelian dengan memanfaatkan atribut DayPart dan DayType. Selanjutnya algoritma FP-Growth dipilih karena kemampuannya  mengidentifikasi sekumpulan item yang sering muncul dalam database transaksional dengan efisiensi tinggi.  Berdasarkan penelitian yang telah dilakukan, penelitian ini merekomendasikan 6 rules yang diambil masing -masing dataset. Aturan-aturan yang dihasilkan ini nantinya dapat digunakan sebagai rekomendasi bundling produk pada waktu tertentu.  Untuk mendapatkan rule yang kuat dalam data transaksi toko roti dimasukkan nilai minimum support berkisar diantara 0.02 (20%) – 0.06 (60%) dan nilai minimum confidence diantara 0.03 (30%) – 0.06 (60%) semua rules yang di diperoleh memiliki nilai nilai lift ratio lebih dari 1 yang menunjukkan adanya korelasi dan manfaat dari rules tersebut. Penentuan nilai minimum support dan confidence dipengaruhi dari jumlah transaksi yang terkandung pada setiap datasetKata kunci: Data Transaksi, Data Mining, Teknik Asosiasi, FP – Growth, Lift Ratio AbstractThis study aims to explore the use of association techniques to identify hidden patterns in sales transaction data. The analysis of customer purchasing patterns contained in transaction data is conducted using a dataset from a bakery. The dataset includes key attributes such as transaction number, item, and time-related variables such as daytime, daypart, and daytype. To maximize recommendation results, the dataset is divided into six segments based on purchase time using the DayPart and DayType attributes. The FP-Growth algorithm is selected due to its efficiency in identifying frequently occurring itemsets within transactional databases. Based on the conducted analysis, the study recommends six rules derived from each dataset segment. These rules can be used for product bundling recommendations at specific times. To obtain strong rules, the transaction data of the bakery includes a minimum support value ranging from 0.02 (20%) to 0.06 (60%) and a minimum confidence value ranging from 0.03 (30%) to 0.06 (60%). All obtained rules have lift ratios greater than 1, indicating a correlation and benefit of the rules. The determination of minimum support and confidence values is influenced by the number of transactions within each dataset.Keywords: Transaction Data, Data Mining, Association Techniques, FP – Growth, Lift Ratio.
Co-Authors Arni Sukmiarni BARMAWI, MIRA MUSRINI Corry Caromawati DEWI, IRMA AMELIA LAUTAN, GAMAS BLOORY Levita Dwinaya Maulana, Rifki Miftahuddin, Yusup MOCHAMAD FACHRY PRAYOGA Pandji, Prambudhi Wibowo Putra, Kurnia Ramadhan RAINA, JODI Sofia Umaroh Umaroh, Sofia Wahyudi, Agung Deni