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All Journal Teknika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic INTECOMS: Journal of Information Technology and Computer Science Jurnal Sains dan Teknologi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) International Journal for Applied Information Management AJAD : Jurnal Pengabdian kepada Masyarakat International Journal Software Engineering and Computer Science (IJSECS) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal Innovations Computer Science
Setiawan, Kiki
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Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences

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Application of the Naive Bayes Algorithm in Twitter Sentiment Analysis of 2024 Vice Presidential Candidate Gibran Rakabuming Raka using Rapidminer Amini, Tasya Aisyah; Setiawan, Kiki
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 1 (2024): APRIL 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i1.2236

Abstract

In the current era of digital democracy, social media sentiment analysis has become a relevant method for understanding public views of political figures. As one of the leading social media platforms, Twitter provides a public space for sharing opinions and expressions regarding political issues. This research aims to classify and measure the accuracy of people's responses to the positive and negative sides. Sentiment analysis was carried out using the Naïve Bayes method using a dataset of 3223 tweets. The final results of this research show that implementing the Naïve Bayes Method in sentiment analysis regarding political dynasty polemics, especially regarding the 2024 Cawapres Gibran Rakabuming Raka, provides an accuracy value of 82.19%. Of the 1696 negative and 112 positive sentiments predicted, there were 462 harmful and 953 positive predicted data. These results indicate that most public responses tend to be detrimental to the Constitutional Court's (MK) decision, which grants political legitimacy to Gibran Rakabuming Raka as the 2024 vice-presidential candidate.
Clustering Data Calon Siswa Baru Menggunakan Metode K-Means di Pusat Pengembangan Anak Fajar Baru Cengkareng Setiawan, Kiki; Saputry, Yulia Yanti Ayu
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 8 No 1 (2024): JANUARY-MARCH 2024
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v8i1.1426

Abstract

Clustering is the process of partitioning a set of data objects into subsets known as clusters. K-means is an unsupervised learning algorithm, K-Means also has a function to group data into data clusters. The K-Means algorithm method was chosen because it has a fairly high accuracy of object size, so this algorithm is relatively more scalable and more efficient for processing large numbers of objects. In the world of education, in general, every new school year there will be something called registration of new prospective students, at the Fajar Baru Child Development Center, many prospective students are accepted from 3 years to 5 years old, therefore the authors hope that by using clustering data can easily group data so that it can make it easier to find the necessary data. By using the K-means algorithm method and using the RapidMiner application, it found 80% efficient results in grouping data.
Media Interaktif Pembelajaran Berbasis Multimedia menggunakan Adobe Flash untuk TK dan PAUD Aryanti, Putri Gea; Lailany, Afyra Ar’bah; Amelia, Ika; Regita, Anggit Nur Hannaa; Setiawan, Kiki; Yel, Mesra Betty
AJAD : Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 1 (2024): APRIL 2024
Publisher : Lembaga Mitra Solusi Teknologi Informasi (L-MSTI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59431/ajad.v4i1.282

Abstract

Preschool (TK) or Early Childhood Education (PAUD) plays an essential role in shaping the foundation of knowledge, attitudes, and skills in children. The primary focus of early childhood education goes beyond stimulating, guiding, nurturing, and providing learning activities at school. Media learning, incredibly educational interactive media, becomes crucial in motivating and educating children. This study focuses on developing interactive multimedia-based learning media using Adobe Flash for TK and PAUD at TK Bamadita Rahman. Through interviews with the school principal and teachers, a needs analysis was conducted to evaluate the implementation of interactive learning media. The research addresses conventional learning challenges by designing an engaging, child-friendly interactive media application. The objectives include creating interactive media, serving as a reference for play-based learning activities, implementing interactive media, and assessing its effectiveness in TK and PAUD Bamadita Rahman. The study concludes that the learning application using Adobe Flash has been successfully designed, has the potential to cultivate an interest in animation among the younger generation, and enhances the efficiency of the teaching and learning process.
Komparasi Metode K-Nearest Neighbor Dan Support Vector Machine Menggunakan Particle Swarm Optimization Untuk Analisis Sentimen Produk Skincare Skintific Yrain, Beatrice; Setiawan, Kiki
Jurnal Sains dan Teknologi Vol. 5 No. 1 (2023): Jurnal Sains dan Teknologi
Publisher : CV. Utility Project Solution

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Abstract

Seringkali banyak kesalahan saat memilih skincare karena belum mengenali jenis wajah kita maupun hanya tergiur karenaproduk tersebut sedang viral. Dari pembahasan ini banyak pro dan kontra yang disampaikan melalui sosial media mengenai produkskincare. Melalui penelitian ini akan dilakukan Analisis Sentimen terhadap produk skincare menggunakan metode K-NearestNeighbour dan Support Vector Machine yang akan memberikan hasil uji dan hasilnya akan di optimalkan dengan fitur selection yaituParticle Swarm Optimization. Adapun tahapan yang dilakukan dalam penelitian ini adalah pengumpulan data, pelabelan data, pre-processing data, pembobotan, penerapan metode, pengujian dan diakhiri hasil/evaluasi. Tujuan dari penelitian agar hasil uji akanmenentukan skincare yang baik supaya para konsumen tidak salah lagi memilih jenis skincare dan bertujuan untuk menentukan apakahpenggunaan skincare bersentimen positif atau negatif dan diharapkan bisa menghasilkan nilai akurasi yang baik yang sudah dilakukanoleh 2 metode tersebut, dan didapatkan hasilnya dari metode Support Vector Machine 91,37% sedangkan K-Nearest Neighbour 70,78%.Setelah ditingkatkan nilai akurasinya dengan fitur seleksi maka hasil akurasi yang dihasilkan dari metode Support Vector Machine94,90% sedangkan K-Nearest Neighbour 72,75%. Lalu di dapatkan 667 sentimen positif dan 76 sentimen negatif.
Implementasi Algoritma Naive Bayes Menggunakan Particle Swarm Optimization Untuk Prediksi Penjualan Produk Air Mineral Fricco, Arjun; Setiawan, Kiki
Jurnal Sains dan Teknologi Vol. 5 No. 1 (2023): Jurnal Sains dan Teknologi
Publisher : CV. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kepuasan Pelanggan dalam menjual Air minum Alkali Ph 9 adalah air minum yang di konsumsi oleh tubuh yang mengandung mineral, memiliki rasa yang manis dan ringan. Mengingat penjualan air minum semakin meningkat di setiap daerah selama perkembangannya khususnya dii wilayah Jakarta Pulogebang dan sekitarnya. Saat ini masyarakat sangat berhati hati dan teliti dalam memilih air minum dan harus memiliki ph diatas dari 8, Karena membuat tubuh jauh lebih sehat bugar, berenergi dan vitalitas juga akan meningkat serta menghemat waktu dalam pemesanan. Peningkatan penjualan Air Mineral di toko juga di sebabkan sulitnya mendapatkan air bersih di kota kota yang terdampak kemarau dan kekurangan air bersih pada saat itu. Permasalahan selanjutnya yaitu sulitnya mengalokasikan sumber daya untuk melakukan produksi pada saat permintaan pasar melonjak. Dari permasalahan tersebut, diperlukan suatu prediksi sebagai alat penunjang pengambilan keputusan untuk mengetahui perkiraan permintaan pasar. Pemanfaatan Algoritma Naïve Bayes akan di gunakan pada data penjualan air mineral sehingga di hasilkan suatu prediksi / perkiraan. Berdasarkan prediksi Algoritma Naïve Bayes Produsen ingin mengalokasikan sumber daya untuk memenuhi kebutuhan Konsumen.
IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN UNTUK MENGETAHUI POLA PEMBELIAN KONSUMEN PADA KOPERASI IDN BOARDING SCHOOL Abdullah; Setiawan, Kiki
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.390

Abstract

School cooperatives are established to fulfill common interests and meet the needs of students. The transaction data of a cooperative is increasing day by day, but sometimes in a cooperative the data is left without any further utilization, as happened in the IDN Boarding School Jonggol cooperative. Utilization of the collected data can produce new information that can be used as a reference for cooperatives in determining a business strategy. Therefore, research was conducted on sales transaction data in the IDN Boarding School cooperative as an object to be analyzed using data mining techniques with an a priori algorithm, resulting in a pattern of consumer purchases that occur in the Jonggol IDN Boarding School cooperative. The transaction data used is transaction data at the IDN Boarding School cooperative. This study succeeded in implementing data mining techniques using the a priori algorithm in an application that will be used to process available transaction data, resulting in consumer purchasing patterns that tend to occur from a combination of available items. The association pattern that is formed with a minimum support of 40% and a minimum confidence value of 70% produces 9 association rules. The strong rule obtained is that if you buy a white uniform, you will buy a brown uniform with a support value of 50% and a confidence value of 83.33% items.
Implementasi Algoritma Naïve Bayes Terhadap Data Penjualan untuk Mengetahui Pola Pembelian Konsumen pada Kantin Rosidi, Raihan Putra Mohammad; Setiawan, Kiki
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i1.407

Abstract

Implementation of the Naïve Bayes algorithm on sales data to determine consumer buying patterns in canteens is an approach that seeks to overcome the limited understanding of consumer behavior in canteens. The goal of this approach is to analyze sales data and identify purchasing patterns to gain insight into consumer behavior, optimize sales strategies, and improve customer satisfaction. By applying the Naïve Bayes algorithm to sales data, this research seeks to identify factors that influence consumer behavior, such as price, convenience, and menu offerings, and provide insights that can be used to optimize menu offerings, pricing strategies and resource allocation. Additionally, the approach seeks to increase customer satisfaction and differentiate canteens from competitors by personalizing menu offerings and enhancing the overall customer experience. This research also aims to contribute to the field of consumer behavior research by applying the Naïve Bayes algorithm to canteen sales data, potentially providing insights that can be applied in other contexts. Overall, the application of the Naïve Bayes algorithm to canteen sales data can provide a data-driven approach to understanding consumer behavior and improving sales strategy and customer satisfaction.
Pengembangan Game 2D Platformer Berbasis Microbit Menggunakan Unity Saputra, Rizqi Kurniawan; Setiawan, Kiki
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i1.420

Abstract

This research aims to develop a 2D platformer game using the Microbit platform and Unity as the development tools. The Microbit serves as the controller with various sensors and computational capabilities. The development process starts with designing the game concept, including level design, characters, and mechanics. Unity is used to create levels, game logic, and connect the Microbit to the game. Users can control the character through the sensors and buttons on the Microbit. Testing involves participants to measure the game's quality and effectiveness and gather feedback. The collected data is used to analyze the strengths and weaknesses of the game and make iterations to enhance the gaming experience. As a result, the researchers have created a game that can be controlled via Microbit, enriching the gaming experience.
Sentiment Analysis of Cigarette Use Based on Opinions from X Using Naive Bayes and SVM Tundo; Eldina, Ratih; Setiawan, Kiki; Fajri, Raisah
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.947

Abstract

The research employs Naive Bayes and Support Vector Machine (SVM) classification techniques to analyze attitudes toward cigarette consumption based on Twitter user opinions. Twitter, being one of the most popular social media platforms, serves as an excellent source for gauging public sentiment on various issues, including cigarette smoking, referred to here as "X." The diverse array of opinions poses a challenge for accurate sentiment classification. This study evaluates the effectiveness of the Naive Bayes and SVM algorithms in categorizing sentiment as positive, negative, or neutral. Data is collected through web scraping, and preprocessing steps such as text cleaning, tokenization, and stemming are implemented. The performance of the classification is assessed using metrics like accuracy, precision, recall, and F1-score. The results indicate that SVM outperforms Naive Bayes in sentiment analysis related to cigarette use. These findings provide new insights into public opinion and aim to assist policymakers in developing effective tobacco control strategies.
Implementasi Data Mining Prediksi Penjualan Produk Semen Menggunakan Metode Linear Regression (Studi Kasus PT. Toyo Mortar Indonesia) Badzlin, Rosyiqoh; Setiawan, Kiki
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.959

Abstract

The cement industry in Indonesia plays a vital role in infrastructure development and the construction sector. PT. Toyo Mortar Indonesia, established in 2015 in the Tangerang industrial area, focuses on the production of instant mortar cement. However, the national cement industry faces the challenge of declining sales due to the pandemic, which has affected people's purchasing power and slowed down property and infrastructure projects. To achieve future growth potential, the right strategy is needed in sales planning. This study implements data mining using the linear regression method with the RapidMiner application to predict cement product sales. The data used includes historical sales data and product information from PT. Toyo Mortar Indonesia. In the tests conducted, the data was processed using 12 variables, of which 10 variables influenced the prediction results: TM-168, TM-178, TM-188, TM-189, TM-198, UM-100, UM-101, UM BIRU, raw materials, and other sales. The test results show that the Linear Regression method can predict cement product sales with an average Root Mean Square Error (RMSE) value of 493,125.701 with a standard deviation of 126,330.525. The average Absolute Error is 303,270.965 with a standard deviation of 46,207.586, and the average relative error is 1.89% with a standard deviation of 0.36%, indicating very good prediction accuracy. This can help the company in making decisions related to sales planning using the available sales data.
Co-Authors Abdullah Aimar, Muqorrobin Akbar, Yuma Alafia, Evi Alifia, Tiara Ratu Amelia, Ika Amini, Tasya Aisyah Apriyanto, Kevin Jonathan Ardiyansyah, Ferry Aryanti, Putri Gea Aula, Raisah Fajri Azhar, Anisah Nurul Badzlin, Rosyiqoh Betty Yel, Mesra Eldina, Ratih Fadillah, M. Rafli Fricco, Arjun Ghazi, Ahmad Kastum Lailany, Afyra Ar’bah Marindah, Marindah Miswanto Miswanto Mufid, Ahmad Raihan Nabilah, Laila Nauvalsalam, Nauvalsalam Oktavian, Nur Pratama, Yuliya Putri Purwasih, Intan PUTRI WULANDARI Qibthiyah, Mariyatul Raharjo, Pratama Putra Rahmawati, Dwita Regita, Anggit Nur Hannaa Rodhiyah Rodhiyah Rosidi, Raihan Putra Mohammad Salam, Nauval Saputra, Rizqi Kurniawan Saputry, Yulia Yanti Ayu Sarikah, Dede Sarimole, Frencis Matheos Sibarani, Julvan Marzuki Putra Sodik Sri Lestari Surapati, Untung Sutisna Sutisna, Nandang Syaputra, Ilham Adi Tundo, Tundo Yrain, Beatrice Zakaria, Aditya Zidan, Muhammad Rohyan