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Analisis Sentimen Aplikasi Tiktok Tokopedia Seller Center dengan Pendekatan Machine Learning: SVM, CNN, Naive Setiawati, Neha Poetri; Nurmalitasari, Nurmalitasari; Atina, Vihi
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 1 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i1.7189

Abstract

Tiktok Tokopedia Seller Center merupakan aplikasi yang diperuntukan untuk para pelaku usaha atau para penjual mengenai pengelolaan dan meningkatkan penjualan mereka pada platform Tiktok (Tiktok shop). Penelitian ini memiliki tujuan untuk melakukan analisis sentimen negatif dan positif dari pengguna mengenai fitur dan kinerja dari aplikasi Tiktok Tokopedia Seller Center dengan menggunakan 3 algoritma Machine Learning, diantaranya SVM, CNN, Naive Bayes. Dikarenakan algoritma ketiga tersebut dinilai efektif dalam menangani kasus analisis sentimen. Data untuk penelitian ini diperoleh dari ulasan aplikasi Tiktok Tokopedia Seller Celler center yag terdapat di Google Play Store. Hasil penelitian menunjukkan bahwa SVM memiliki kinerja dan kinerja yang lebih unggul dibandingkan CNN, dan Naive Bayes, karena SVM memperoleh akurasi akhir sebesar 0.90 , disusul CNN dengan akurasi sebesar 0.88 dan Naive Bayes dengan akurasi sebesar 0.84 . Dari penelitian ini dapat memberikan gambaran atau wawasan bagi pengembang aplikasi Tiktok Tokopedia Seller Center dalam melakukan perbaikan dan meningkatkan kualitas pelayanan .
Analisis Sentimen Ulasan Pengguna pada Aplikasi SIREKAP 2024 Menggunakan Machine Learning Hidayat, Taufik; Nurchim, Nurchim; Nurmalitasari, Nurmalitasari
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 1 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i1.7021

Abstract

Aplikasi Sirekap 2024 adalah sebuah aplikasi baru yang digunakan untuk mencatat dan melaporkan kegiatan dan hasil pemungutan suara pada Pemilihan Umum di Indonesia tahun 2024. Terdapat berbagai pandangan pro dan kontra terkait penggunaan aplikasi ini, sehingga aplikasi ini dipilih sebagai objek penelitian. Penelitian ini bertujuan untuk mengklasifikasikan ulasan pengguna ke dalam kategori sentimen positif, negatif, dan netral menggunakan tiga algoritma pembelajaran mesin yang berbeda, serta membandingkan dan menentukan algoritma terbaik. Tiga algoritma yang digunakan dalam penelitian ini adalah Naive Bayes, K-Nearest Neighbors (KNN), dan Support Vector Machine (SVM). Metode penelitian mencakup tahap pengumpulan data ulasan, preprocessing data, evaluasi hasil, dan penentuan dan perbandingan metode terbaik. Berdasarkan nilai akurasi, hasilnya adalah Naive Bayes (83%), K-Nearest Neighbors (56%), dan Support Vector Machine (84%). Support Vector Machine (SVM) terbukti menjadi algoritma terbaik dalam analisis ulasan pengguna aplikasi Sirekap 2024 karena memiliki nilai akurasi tertinggi yaitu (84%).
PENERAPAN SISTEM REKOMENDASI CONTENT BASED FILTERING PADA PEMILIHAN SKINCARE DI CAMELIA Alliya, Sania Qorrii; Widyaningsih, Pipin; nurmalitasari, Nurmalitasari
Infotech: Journal of Technology Information Vol 10, No 2 (2024): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v10i2.280

Abstract

Skincare is a vital activity to maintain or improve the condition of facial skin. In Indonesia, skincare trends continue to grow due to various factors such as skin problems, technological advances, research on certain substances, and increasing awareness of the importance of maintaining healthy skin. Camelia skincare, a skincare store that has a website that displays many products on the camelia website without any search or recommendation system, making it difficult for visitors to choose the right product. Therefore, researchers want to build a skincare product recommendation system based on product data on the website using the content-based filtering method and the naïve bayes algorithm for calculating the similarity value. This research uses primary and secondary data from existing data on the website. The development method used is the Agile Software Development method. The agile software development method has several stages, namely planning, implementation, testing, documentation, deployment and maintenance. In conducting the research, researchers used the observation method by looking at existing data on the camelia website. As for the resulting features themselves are skincare search, skincare list, skincare details, and also skincare recommendations. For testing the system itself using the recall and precision table method. And from these tests, the average percentage of recall value is 89% and the percentage of precision value is 96%. ABSTRAKSkincare merupakan kegiatan vital untuk menjaga atau memperbaiki kondisi kulit wajah. Di Indonesia, tren perawatan kulit terus berkembang karena berbagai faktor seperti permasalahan kulit, kemajuan teknologi, penelitian terhadap zat tertentu, dan meningkatnya kesadaran akan pentingnya menjaga kesehatan kulit. Camelia skincare, toko perawatan kulit yang memiliki website yang menampilkan banyak produk di website camelia tanpa ada sistem pencarian ataupun rekomendasi sehingga menyulitkan pengunjung dalam memilih produk yang sesuai. Oleh karena itu, peneliti ingin membangun sistem rekomendasi produk skincare berdasarkan data produk pada website tersebut dengan menggunakan metode content based filtering dan algoritma naïve bayes untuk perhitungan nilai similarity. Penelitian ini menggunakan data primer dan sekunder dari data yang ada pada website. Metode pengembangan yang digunakan adalah metode Agile Software Development. Metode pengembangan sistem agile software development memiliki beberapa tahapan, yaitu planning, implementation, testing, documentation, deployment dan maintenance. Pada pelaksanaan penelitian, peneliti menggunakan metode observasi dengan melihat data yang sudah ada pada website camelia. Sedangkan untuk fitur yang dihasilkan sendiri adalah pencarian skincare, list skincare, detail skincare, dan juga rekomendasi skincare. Untuk pengujian sistemnya sendiri menggunakan metode tabel recall dan  precision. Dan dari pengujian tersebut didapatkan rata-rata prosentase nilai  recall yaitu 89% dan prosentase nilai precision yaitu 96%.
PREDIKSI HARGA SAHAM PT BANK CENTRAL ASIA TBK DENGAN MENGGUNAKAN ALGORITMA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) Tri Wulandari, Rahmawati Desi; Nurmalitasari, Nurmalitasari; Permatasari, Hanifah
Infotech: Journal of Technology Information Vol 10, No 2 (2024): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v10i2.278

Abstract

Stock prediction is one of the important things in stock trading. It calculates the potential worth of stocks or other financial assets that are traded on stock exchanges. The precision of stock forecast outcomes serves several purposes, principally providing investors with protection from possible market hazards and opening doors for index traders, market speculators, and arbitrageurs to make money. This study used the Autoregressive Integrated Moving Average (ARIMA) model and the Google Colab website to examine and predict the closing price of PT Bank Central Asia Tbk shares. This study uses daily historical stock data from 01 January 2020 to 01 January 2024. The ARIMA (1.0.1) model is the most accurate model for predicting the closing price of PT Bank Central Asia Tbk shares, according to the study’s findings.ABSTRAKDalam trading saham, Prediksi saham adalah hal yang sangat penting. Mengetahui nilai masa depan saham perusahaan atau instrument keuangan lainnya yang diperdagangkan di bursa keuangan adalah sesuatu yang menarik. Banyak hal dipengaruhi oleh akurasi hasil prediksi saham, terutama kebutuhan bagi investor untuk melindungi nilai mereka dari resiko pasar yang mungkin terjadi, peluang bagi speculator pasar dan arbitrase mengambil keuntungan dari indeks perdagangan. Tujuan penelitian ini untuk memprediksi harga penutupan saham PT Bank Central Asia Tbk dengan menggunakan model Autoregressive Integrated Moving Average (ARIMA) dan menganalisisnya dengan menggunakan website Google Colab. Penelitian ini menggunakan data historis harian saham dari tanggal 1 Januari 2020 hingga tanggal 1 Januari 2024. Hasil studi menunjukkan bahwa model ARIMA (1.0.1) adalah yang terbaik untuk meramalkan harga penutupan saham pada perusahaan ini.
Prediksi Churn Pelanggan Multinational Bank Menggunakan Algoritma Machine Learning Hidayat, Rifki; Syawaludin, M Ainur; Nurmalitasari, Nurmalitasari
Simpatik: Jurnal Sistem Informasi dan Informatika Vol. 4 No. 2 (2024): Desember 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/simpatik.v4i2.4595

Abstract

Dalam menghadapi persaingan pasar yang ketat, prediksi churn pelanggan menjadi penting bagi perusahaan perbankan untuk mempertahankan loyalitas pelanggan. Penelitian ini mengaplikasikan algoritma machine learning meliputi Naive Bayes, Decision Tree, dan Random Forest untuk prediksi churn pelanggan pada ABC Multinational Bank. Data yang digunakan adalah dataset publik yang diambil dari Kaggle yang mencakup informasi 10.000 nasabah bank. Proses penelitian melibatkan beberapa tahapan yaitu pengumpulan data, preprocessing, pemodelan, prediksi, dan evaluasi. Hasil evaluasi memperlihatkan bahwa model Random Forest memberikan performa terbaik dengan akurasi 85% dan AUC 0.83. Naive Bayes dan Decision Tree masing-masing memiliki akurasi 82% dan 77%. Kesimpulan menunjukkan bahwa Random Forest lebih unggul dalam memprediksi churn pelanggan dibandingkan dua algoritma lainnya, sehingga dapat digunakan untuk strategi pemasaran yang lebih efektif dalam industri perbankan.
Empowering Asian Students Through Artificial Intelligence: A Workshop on Predicting Plant Growth to Support Smart Farming Nurchim, Nurchim; Nurmalitasari, Nurmalitasari
International Journal Of Community Service Vol. 5 No. 1 (2025): February 2025
Publisher : CV. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijcs.v5i1.839

Abstract

The integration of Artificial Intelligence (AI) in agriculture has revolutionized traditional farming practices, enhancing productivity, efficiency, and sustainability. This study highlights a workshop aimed at equipping students with practical AI skills, specifically focusing on linear regression techniques for crop growth prediction. The workshop, involved 55 students from nine Asian countries, fostering cross-cultural collaboration. Participants were introduced to theoretical concepts and engaged in hands-on training, covering data preprocessing, region of interest extraction, and model implementation using Python. The program emphasized the role of AI in addressing agricultural challenges such as resource optimization and food security. The workshop was conducted in five stages: preparation, implementation, evaluation, dissemination, and participant engagement. Pre and post-test evaluations revealed a significant improvement in participants’ AI knowledge, with average scores increasing from 45% to 85%. Practical activities enabled students to connect theoretical knowledge with real-world applications, enhancing their ability to predict crop growth using AI techniques. Dissemination efforts included reports and publications to inspire similar global initiatives. The results demonstrated the workshop's effectiveness in bridging knowledge gaps, fostering sustainable agricultural practices, and preparing a skilled workforce capable of leveraging AI to address future challenges in smart farming.
Implementasi Prediksi Penjualan Powderindo Menggunakan Metode Single Moving Average Nugroho, Mohammad Yusuf; Nurmalitasari, Nurmalitasari; Nurchim, Nurchim
Innovative: Journal Of Social Science Research Vol. 5 No. 2 (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.v5i2.18297

Abstract

Industri food and beverage merupakan salah satu sektor yang berkontribusi dalam pemenuhan kebutuhan konsumen. Salah satu usaha di bidang ini adalah penjualan bubuk minuman dengan merek PowderIndo, yang menawarkan berbagai varian rasa. Namun, ketidakstabilan tingkat penjualan akibat tren atau perubahan musim menjadi tantangan bagi pelaku usaha. Oleh karena itu, penelitian ini bertujuan untuk meramalkan penjualan bubuk PowderIndo menggunakan metode Single Moving Average. Langkah-langkah penelitian meliputi (1) pengumpulan data aktual, (2) pre-processing, (3) pemodelan, dan (4) perhitungan tingkat kesalahan (error). Data yang digunakan dalam penelitian ini merupakan data transaksi penjualan selama tiga bulan. Hasil dari penelitian ini menunjukkan bahwa metode Single Moving Average dapat digunakan untuk meramalkan penjualan PowderIndo dengan tingkat kesalahan yang rendah, yaitu RMSE = 0,24, MAE = 0,15, dan MAPE = 8,68%. Berdasarkan hasil prediksi, jumlah penjualan pada minggu berikutnya diperkirakan mencapai 76 produk. Oleh karena itu, disarankan agar penelitian selanjutnya mempertimbangkan strategi penambahan stok produk guna mengantisipasi permintaan pada periode yang akan datang.
Analisis Sentimen Model Distilbert Multilingual Cased Dalam Mengklasifikasikan Ulasan Game Genshin Impact Sajad, Abdullah; Nurmalitasari, Nurmalitasari; Purwanto, Eko
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 2 (2024): June 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid.16.2.2024.124-136

Abstract

The evolution of information technology has revolutionized how humans engage with the world, particularly within the gaming sector. This paper explores the utilization of the DistilBERT Multilingual Cased model for analyzing sentiments expressed in Genshin Impact game reviews. The research methodology encompasses gathering data from Google PlayStore and Apple AppStore, manually labeling data, preprocessing it, and employing the DistilBERT Multilingual Cased model for analysis. The model's performance is assessed using metrics such as accuracy, precision, recall, and f1-score. Findings reveal that the model effectively categorizes sentiment in reviews, achieving an overall accuracy of 82%. Precision, recall, and f1-score metrics consistently surpass 0.77 across all sentiment categories. This study concludes that the DistilBERT Multilingual Cased model shows promise as a valuable tool for multilingual sentiment analysis within the realm of game reviews.
Rancang Bangun Sistem Informasi Inventory Yang Dilengkapi Oleh Peramalan Stock Inventory Menggunakan Metode Autoregresif Integrated Moving Average (ARIMA) (Studi Kasus : UD.SANUSI) Utami, Maulinda Devi; Purwanto, Eko; Nurmalitasari, Nurmalitasari
Jurnal Informa : Jurnal Penelitian dan Pengabdian Masyarakat Vol 7 No 2 (2021): Desember
Publisher : Politeknik Indonusa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46808/informa.v7i2.205

Abstract

Trading business is an activity of buying and selling goods or services with the aim of making a profit, without changing the condition of the product being sold. There are many types of trading businesses in Central Java, one of which is a building material equipment trading business trading business which is engaged in building material equipment itself carries out buying and selling activities for building goods such as stones, meters, screwdrivers, etc. UD.SANUSI is a trading business engaged in building tools owned by individuals. UD.SANUSI continues to grow today, it affects the number, types of items, and existing purchases. Due to the large number of requests, UD.SANUSI tries to meet market demand by completing the types of goods and stock available at the store. With advances in existing technology, it can be facilitated with an information system that will help the process of predicting goods using the ARIMA (Autoregressive Integrated Moving Average) calculation model. from the sales data obtained from the calculation results, ACF and PACF can be analyzed that ACF (p,q) is cut after lag 2 (q - p) and PACF is cut after lag 2 (p - q) with the results of P-Value AR (1 ) 0.000, P-Value MA (1) 0.000, P-Value Constant 0.019 and MSE of 20.9631.
Penerapan Metode Regresi dalam Memprediksi Laba Bersih Penjualan Sari, Nur Avia Adenta; Nurmalitasari, Nurmalitasari; Nurchim, Nurchim
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.7986

Abstract

Unexpected changes in net profit can make it difficult for XYZ to control costs and make strategic decisions. This study proposes the use of linear regression as a forecasting method for net sales profit as a way to reduce uncertainty in financial planning and assist in business decision-making. This approach was chosen because of its ability to measure and explain the correlation between dependent factors, such as net profit, and independent variables, such as sales volume and operating costs. The analysis procedure includes data exploration, regression modeling, model performance evaluation, and visualization of prediction results, which can be conducted systematically. Considering the findings of this study's evaluation, the model demonstrated excellent performance in predicting net sales profit, with an evaluation result of Root Mean Squared Error (RMSE) of 27778.50, Mean Absolute Error (MAE) of 20084.71, Mean Absolute Percentage Error (MAPE) of 9.88 %. The main advantage of this forecasting is its ability to help businesses improve the accuracy of financial planning, manage operational costs, and develop focused business plans. Additionally, management can avoid overstocking or understocking, set reasonable sales targets, and adjust production levels to market demand through the use of accurate forecasts.
Co-Authors Adenta Sari, Nur Avia Al Mustofa, Muhammad Hafizh Alliya, Sania Qorrii Atina, Vihi Awang Long, Zalizah Dwi Hartanti Eko Purwanto Hasanah, Herliyani Imam Sujadi JOKO SUSANTO Miftakhul Jannah, Ulfarida Nugroho Arif Sudibyo Nugroho, Mohammad Yusuf Nurchim Nurchim Permatasari, Hanifah Pipin Widyaningsih Rifki Hidayat Sajad, Abdullah Sari, Nur Avia Adenta Setiawati, Neha Poetri Singgih Purnomo Syawaludin, M Ainur Taufik Hidayat Tri Atmojo Kusmayadi Tri Wulandari, Rahmawati Desi Utami, Maulinda Devi