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Nurapriani, Fitria
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Classification of Reject Patterns Based on Production Stages Using the K-Means Clustering Method Lestari, Renita; Novalia, Elfina; Tukino; Nurapriani, Fitria
Golden Ratio of Data in Summary Vol. 5 No. 4 (2025): August - October
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grdis.v5i4.1301

Abstract

This study aims to classify reject patterns in the production process using the K-Means Clustering method. The dataset consists of 870 records collected from the production line, containing information such as product name, reject type, process stage, and production quantity. Through a data mining approach, data preprocessing steps such as cleaning, encoding, and normalization were performed prior to the clustering process. The Elbow Method indicated that the optimal number of clusters is three. Each cluster exhibits distinct characteristics: light rejects with small quantities in early stages, heavy rejects with large quantities, and moderate rejects with random distribution. These findings are expected to assist management in formulating more targeted strategies for process improvement and quality control. By identifying common reject patterns within each cluster, companies can adopt a more proactive approach to minimizing production defects and enhancing overall operational efficiency.
Integrasi Etnomatematika dalam Pembelajaran Bangun Datar Segi Empat Berbasis Kearifan Lokal untuk Meningkatkan Pemahaman Matematika Lestari, Santi Arum Puspita; Kusumaningrum, Dwi Sulistya; Nurapriani, Fitria
Jurnal Inovasi Penelitian dan Pengabdian Masyarakat Vol. 4 No. 2 (2024): Desember
Publisher : Indonesia Emerging Literacy Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53621/jippmas.v4i2.369

Abstract

Matematika dianggap sebagai pelajaran wajib dari tingkat pendidikan dasar hingga tinggi karena menjadi dasar dan penghubung bagi mata pelajaran lainnya. Namun, masih ada siswa yang mengalami kesulitan dan memandang matematika hanya sebagai perhitungan dasar. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman matematika siswa dengan menghubungkan matematika dan kebudayaan lokal melalui etnomatematika yang berfokus pada bangun datar segi empat. Kegiatan PkM menggunakan metode sosial konstruktivisme yang dibagi menjadi 5 tahap yakni identifikasi masalah, kolaborasi, eskplorasi, implementasi, dan evaluasi. Melalui kegiatan pengabdian kepada masyarakat, dilakukan penyuluhan di SMPN 2 Cilebar, memperkenalkan etnomatematika pada bidang segi empat kepada siswa. Hasilnya menunjukkan bahwa 85% siswa (22 dari 25 siswa) mampu mengenali bentuk segi empat pada rumah adat Sunda. Selain itu, kegiatan ini berhasil meningkatkan minat siswa terhadap matematika dengan mengaitkannya secara nyata dengan kebudayaan lokal. Meskipun berhasil, masih ada faktor penghambat, seperti persepsi sulitnya matematika dan pandangan bahwa matematika bersifat abstrak. Dengan demikian, kesimpulan yang diperoleh dari kegiatan ini adalah memberikan kontribusi positif dalam memahamkan siswa mengenai penerapan matematika pada kehidupan sehari-hari melalui pendekatan etnomatematika.
Analisis Sentimen, Text Mining Penerapan Analisis Sentimen Dan Naive Bayes Terhadap Opini Penggunaan Kendaraan Listrik Di Twitter Agustian, Adittia; Tukino; Nurapriani, Fitria
Jurnal Tika Vol 7 No 3 (2022): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v7i3.1550

Abstract

Twitter is the most popular social media today. Can find out various Twitter responses that fall into the positive, neutral, or negative categories. Technological advances at this time are so rapid that vehicles will provide fuel for electric power or are called electric vehicles. Indonesia has become a country that encourages acceleration in the use of electric vehicles, according to the Minister of State-Owned Enterprises circular letter. The advancement of electric-powered vehicles is an innovation and technology that will continue to develop and transform. With the presence of the electric vehicle, the Indonesian government will serve as an important guest vehicle at the G20 Summit activities in Bali, Indonesia. The purpose of this study is to determine the public's response to electric vehicles which are currently widely used among the people of Indonesia. To find out the public response, sentiment analysis is needed through the responses of Twitter users. By generating positive, neutral, or negative categories. Based on the results of the classification of sentiment analysis on the support of electric vehicles. Data collection uses the Twitter API as an open source that can retrieve Twitter user responses, then the data cleaning process is carried out, converting Indonesian to English, then tested using the Naïve Bayes algorithm, and visualizing twitter data using python. Based on the classification results, public response to electric vehicles is more positive with 82% precision and 44% recall. By having 80% data accuracy through the Naive Bayes confusion matrix through the text mining process, python text blob, and word cloud as the relationship between words and twitter text
Clustering User Sentiment Transportasi Online Gojek Dan Grab Dengan Metode K-Means Annam, Dyno Syaiful; Hananto, Agustia; Nurapriani, Fitria; Tukino, Tukino
Jurnal Tika Vol 8 No 2 (2023): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v8i2.2165

Abstract

As an online transportation service, people often discuss it by sharing their opinions through various social media platforms, one of which is Google Play reviews. The opinions given by the public regarding online transportation services are diverse. Users provide reviews about the application, and naturally, users will choose an application with good reviews. However, monitoring the opinions of the general public is not easy, given the large volume of data to be processed. Therefore, the researcher aims to obtain accurate and precise information from user reviews of Gojek and Grab using clustering techniques, specifically the K-means method, using the RapidMiner application. The results of the testing of both applications can be summarized as follows: Gojek and Grab receive reviews that are not significantly different, although Grab's reviews are slightly better. The classification using the K-Means method offers a solution to the issue of sentiment analysis in user reviews of online transportation applications.
Analisis Sentimen Aplikasi Layanan Streaming Pada Google Play Store Menggunakan Algoritma Naïve Bayes Alparizi, Muhamad Iqbal; Hananto, April Lia; Nurapriani, Fitria; Huda, Baenil
Innovative: Journal Of Social Science Research Vol. 5 No. 3 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i3.19093

Abstract

Aplikasi streaming seperti Amazon Prime Video, Disney+ Hotstar, Catchplay+, Netflix, dan Viu semakin populer seiring dengan berkembangnya industri streaming. Ulasan pengguna di Google Play Store menyediakan informasi penting mengenai persepsi dan kepuasan terhadap aplikasi-aplikasi tersebut. Penelitian ini bertujuan untuk menganalisis sentimen dari lima aplikasi streaming dengan memanfaatkan algoritma Naive Bayes. Ulasan pengguna dikategorikan menjadi sentimen positif dan negatif. Proses klasifikasi dievaluasi dengan algoritma Naive Bayes, diukur menggunakan metrik akurasi, presisi, recall, dan skor F1. Hasil penelitian menunjukkan bahwa Amazon Prime Video mencapai akurasi tertinggi sebesar 86% dan skor F1 0,88, disusul oleh Catchplay+ dengan akurasi 83% dan skor F1 0,87. Catchplay+ memiliki ulasan positif tertinggi sebanyak 730 ulasan, sementara Disney+ Hotstar memperoleh ulasan negatif terbanyak dengan 776 ulasan. Temuan ini mengindikasikan bahwa algoritma Naive Bayes efektif dalam menganalisis sentimen aplikasi streaming serta dapat dimanfaatkan untuk meningkatkan kualitas layanan berdasarkan masukan pengguna.
PERANCANGAN SISTEM INFORMASI INVENTORI STOK BARANG GUDANG KAIN PADA PT.KARY INDOMAS ELOK Haikal, Fikri; Hananto, Agustia; Nurapriani, Fitria; Tukino, Tukino
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 4 No. 3 (2023)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v4i3.2014

Abstract

Abstrak : PT. kary indomas elok yang beralamat di jl. Mitra selatan 2, kav 89,Kawasan industry mitra karawang, karawang, jawa barat 41361, sebuah p[erusahaan textile yang bergerak dibidang pembuatan kain syang berperan dalam memenuhi kebutuhan dalam negeri dan menjadi salah satu perusahaan yang tetap bertahan dalam padatnya persaingan. Dalam pengelolaan data di Gudang kain PT.kary indomas elok textile yaitu depatement yang menyediakan kain putih polos untuk di proses menjadi kain selimut berwarna. Masih dilakukan secara manual dengan alat tulis sehingga informasi yang diperoleh sangat kurang akurat dan membutuhkan waktu yang cukup lama yang menhambat proses bisnis yang berjalan. Penelitian ini bertujuan menganalisis proses bisnis perusahaan yang sedang berjalan, memberikan rekomendasi serta membangun sistem informasi inventori Gudang kain yang dapat mendukung operasional perusahaan. Untuk makai itu dibuatlah sistem informasi inventori Gudang kain untuk mencatat data transaksi pembelian,penjualan, dan stok barang yang dapat mendukung operasiona perusahaan. Penelitian ini menghasilkan sebuah sistem informasi inventori yang dapat memudahkan pencatatan pembelian,penjualan, dan data barang, hasil akhur dari penelitian ini dibuatlah sistem informasi inventori Gudang kain PT. Kary indomas Elok. Kata kunci : Membangun,sistem informasi, Inventori, Gudang kain, PIECES. Abstract : PT. kary indomas elok which is located at jl. Mitra Selatan 2, kav 89, Mitra Karawang industrial area, Karawang, West Java 41361, a textile company engaged in the manufacture of blankets that plays a role in meeting domestic and foreign needs and is one of the companies that survives in dense competition . In data management at PT. Kary Indomas Elok Textile's fabric warehouse, namely Gepatement, which provides plain white cloth to be processed into colored quilt fabric caddies. It is still done manually with stationery so that the information obtained is very inaccurate and takes quite a long time which hampers ongoing business processes. This study aims to analyze the company's ongoing business processes, provide recommendations and build a fabric warehouse inventory information system that can support the company's operations. For this reason, a fabric warehouse inventory information system was created to record purchase, sales and stock transaction data that can support the company's operations. This research produces an inventory information system that can facilitate the recording of purchases, sales, and goods data, the final result of this research is the fabric warehouse inventory information system of PT. Indomas Elok Keyword : Building, Information System, Inventory, Warehouse of Fabrics, PIECES
Analisis Sentimen Ulasan Pengguna Alikasi Traveloka Pada Google Play Store Menggunakan Algoritma Naive Bayes Ikhsan, Muhammad Daffa; Huda, Baenil; Hananto, Agustia; Nurapriani, Fitria
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30444

Abstract

The advancement of the digital era has driven increased usage of online reservation applications, including Traveloka. The abundance of user feedback available on the Google Play Store platform has the potential to become a valuable database for development teams in improving service quality. However, the characteristics of unstructured and spontaneous reviews pose challenges in conventional data processing.This research aims to explore sentiment in Traveloka application user comments using the Multinomial Naïve Bayes algorithm. The dataset used consists of 1,500 review samples obtained through web scraping techniques from the Google Play Store. The research methodology includes several data preprocessing stages, including data cleaning, case normalization, word tokenization (tokenizing), stopword removal, and word stemming to their base forms (stemming). Subsequent processes include data categorization, feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) approach, and building a classification model with the Multinomial Naïve Bayes algorithm.Test results show that the model is capable of classifying sentiment with an accuracy rate of 79%. The model demonstrates high recall values in identifying negative reviews (0.97), but the recall value for positive reviews remains limited (0.64). This indicates that the model has higher sensitivity to negative expressions. From a total of 1,500 review data, there were 461 positive reviews and 543 negative reviews that were successfully categorized clearly.The findings in this study prove that the implementation of the Multinomial Naïve Bayes algorithm is quite efficient in sentiment classification of user reviews, and is capable of providing strategic insights that can be utilized by development teams to improve application service quality
Pemilihan Platform Film Streaming Menggunakan Metode SMARTER dan MOORA: Selection of Streaming Film Platforms Using the SMARTER Method and the MOORA Saputri, Arini; Hilabi, Shofa Shofiah; Nurapriani, Fitria; Huda, Baenil
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1325

Abstract

Sektor industri perfilman telah menjadi aspek tontonan wajib dalam masyarakat, saat ini film menjadi suatu hiburan yang populer di Indonesia. Kemajuan teknologi dan digitalisasi memfasilitasi akses mudah menonton film, masa transisi dari penggunaan DVD/VCD ke Blu-Ray sebagai media untuk menikmati film yang mendapatkan daya tarik pada masanya. Perkembangan internet dan platform online yang semakin pesat telah mengubah industri dunia perfilman, banyak sekali bermunculan berbagai layanan streaming yang menawarkan kemudahan untuk menonton film kapan saja dan dimana saja. Maraknya kemudahan menonton film streaming dengan tersedianya berbagai platform film masih banyak terdapat perbedaan beberapa aspek baik tampilan maupun layanan yang ditawarkan, sehingga penelitian ini memberikan wawasan dan rekomendasi mengenai opsi streaming yang baik. Dalam penelitian ini menggunakan metode MOORA dan SMARTER Kedua metodologi menghasilkan hasil yang sebanding pada nilai tertinggi yaitu Netflik sebagai platform film streaming paling aman dengan skor 0,421 pada metode SMARTER dan 0,582 pada metode MOORA , dan mengalami selisih perbedaan yang tidak terlalu signifikah terkait peroleh nilai tertinggi kedua, Dimana pada metode SMARTER di peroleh oleh Disney Hotstar dengan nilai 0,377sedangkan pada metode MOORA nilai tertinggi kedua di peroleh oleh Iflix dengan nilai0,297sehingga kedua metode ini sangat ideal untuk digunakan.
Implementasi Algoritma K-Nearest Neighbor untuk Prediksi Penjualan Alat Kesehatan pada Media Alkes: Implementation of the K-Nearest Neighbor Algorithm to Predict Sales of Medical Devices in Medical Devices Nijunnihayah, Uktupi; Hilabi, Shofa Shofiah; Nurapriani, Fitria; Novalia, Elfina
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1326

Abstract

Media Alkes Perusahaan ini bergerak dalam bidang industri Alat Kesehatan. Perusahaan ini menyediakan berbagai produk seperti jarum kursi roda, alat infus, alat monitor tekanan darah, dan lain-lain. Media Alkes juga aktif menerapkan strategi bisnis untuk memenuhi kebutuhan pelanggan. Namun sering terjadi kekurangan stok dan barang menumpuk di dalam perusahaan ini. Peneliti telah mengelola dan menganalisis data penjualan yang ada untuk memahami kebutuhan pelanggan terhadap Alat Kesehatan. Dalam menghadapi tantangan tersebut, peneliti mengusulkan algoritma K-Nearest Neighbor untuk memprediksi penjualan Alat Kesehatan di Media Alat Kesehatan. Informasi mengenai jumlah penjualan Alat Kesehatan dengan kriteria Sangat laris, Cukup laris dan Kurang laris dapat dilihat melalui data penjualan tahun 2020 hingga tahun 2022 pada Media Laporan Penjualan Alat Kesehatan. Penelitian dilakukan dengan menerapkan metode K-Nearest Neighbor (KNN) baik dengan perhitungan secara manual maupun menggunakan sistem RapidMiner. Hasil dari prediksi yang menggunakan sistem RapidMiner menunjukkan tingkat akurasi sebesar 95,00% dari data yang disebut penjualan. Dengan hasil prediksi yang didapat yang Sangat bagus tersebut, metode ini dapat dijadikan sebagai acuan dalam merencanakan penjualan di masa depan. Dengan menerapkan prediksi ini, perusahaan dapat mengelola stok barang dengan secara efisien dan menghindari kehabisan stok serta memuat barang yang tidak diinginkan.
Peningkatan Minat Digital Skill Menggunakan Algoritma K-Medoids Clustering Pada Karyawan Zulfiana, Rizka; Hilabi, Shofa Shofiah; Nurapriani, Fitria; Huda, Baenil
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.4994

Abstract

General Company for Printing Money of the Republic of Indonesia is one of the state-owned enterprises that prints banknotes and other official documents. Perum Peruri also wishes to gain more insight into the technology implemented in the company. The demand for a workforce skilled in the use of technology in the work environment continues to increase over time. Perum Peruri has 16 Digital Skill categories, each of these categories has a high, medium to the lowest interest. In this problem, the data taken has not been grouped, so there is a lack of information about the number of categories that have the highest to lowest interest. By analyzing the specialization data, it will help determine which categories need improvement. The categories in Digital Skills specialization can then be improved by using this information as a reference for designing improvement strategies. Research was conducted using clustering to determine the number of categories that Perum Peruri personnel are interested in. In this study, sales data in Excel format was analyzed, and clusters based on product sales data were created using the K-Medoids approach. Sales information obtained from secondary data that manages employee specialization. Using RapidMiner, the accuracy for the three clusters designated as highest, middle, and lowest based on the clustering results was ascertained. The first cluster of 16 items analyzed consisted of 7 items with the highest ranking, the second cluster had 5 items categorized as medium, and the third cluster had 4 items classified as the lowest. Based on the results, 4 items were categorized as low, indicating the need for a socialization approach to increase interest in the Digital Skill.
Co-Authors -, Tukino Agustian, Adittia Alparizi, Muhamad Iqbal Annam, Dyno Syaiful April Lia Hananto Arkan Hilman Hakim Dwi Sulistya FIKRI HAIKAL Hananto, Agustia Hilabi, Shofa Shofiah Huda, Baenil Ikhsan, Muhammad Daffa kastiawan, Nurhayadi Kurniawan, Muhamad Dicky Kusumaningrum, Dwi Sulistya Lestari, Renita Lestari, Santi Arum Puspita Nijunnihayah, Uktupi Novalia, Elfina Nur ‘Azah Nurfauzi, Oka Muhamad Priatna, Bayu Priyatna, Bayu Ramadan, Mohamad Rizqulloh Saputri, Arini Sriardi, Mohammad Didit Tukino, Tukino Yulistina Nur DS Zulfiana, Rizka