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Implementasi Algoritma K-Means dan Rapidminer untuk Clustering Data Kemiskinan Provinsi Banten Maulida, Ricka; Counsela Evrilia, Sisilia Ratu; Az Zahra, Nabila Rahmania; Haryadi, Deny
Journal of Informatics and Communication Technology (JICT) Vol. 6 No. 1 (2024)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v6i1.246

Dalam skala nasional, jumlah tingkat kemiskinan di Provinsi Banten yaitu rendah. Hal ini dapat dibuktikan pada Maret 2022, Banten memiliki 6,16% penduduk yang hidup dalam kemiskinan, lebih rendah dari rata-rata nasional 9,54% untuk periode waktu tersebut. Pendekatan algoritma K-Means Clustering adalah strategi data mining yang digunakan pada penelitian ini. Data penelitian ini, yang mencakup 8 Kabupaten/Kota dan 3 variabel, diperoleh dari situs Badan Pusat Statistik (BPS) antara tahun 2020 dan 2022. Faktor-faktor yang diperhitungkan adalah populasi warga kurang mampu (ribu jiwa), rata-rata (Mean), jumlah tahun yang dihabiskan di sekolah (tahun), dan jumlah uang yang dikeluarkan per orang setiap tahun (ribu rupiah). Seluruh rangkaian data dianalisis menggunakan Rapidminer, diproses menjadi 3 tingkatan cluster, cluster sedang yaitu (C0), cluster tinggi yaitu (C1), dan cluster rendah yaitu (C2). Perhitungan pada Rapidminer mengungkapkan bahwa Kota Tangerang dan Kota Tangerang Selatan berada di cluster 0, Kabupaten Pandeglang, Kab Lebak, Kab Serang masuk dalam cluster 1, serta Kabupaten Tangerang, Kota Cilegon, Kota Serang berada di cluster 2.

Implementasi Algoritma K-Means dan Rapidminer untuk Clustering Data Kemiskinan Provinsi Banten Maulida, Ricka; Counsela Evrilia, Sisilia Ratu; Az Zahra, Nabila Rahmania; Haryadi, Deny
Journal of Informatics and Communication Technology (JICT) Vol. 6 No. 1 (2024)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v6i1.246

Dalam skala nasional, jumlah tingkat kemiskinan di Provinsi Banten yaitu rendah. Hal ini dapat dibuktikan pada Maret 2022, Banten memiliki 6,16% penduduk yang hidup dalam kemiskinan, lebih rendah dari rata-rata nasional 9,54% untuk periode waktu tersebut. Pendekatan algoritma K-Means Clustering adalah strategi data mining yang digunakan pada penelitian ini. Data penelitian ini, yang mencakup 8 Kabupaten/Kota dan 3 variabel, diperoleh dari situs Badan Pusat Statistik (BPS) antara tahun 2020 dan 2022. Faktor-faktor yang diperhitungkan adalah populasi warga kurang mampu (ribu jiwa), rata-rata (Mean), jumlah tahun yang dihabiskan di sekolah (tahun), dan jumlah uang yang dikeluarkan per orang setiap tahun (ribu rupiah). Seluruh rangkaian data dianalisis menggunakan Rapidminer, diproses menjadi 3 tingkatan cluster, cluster sedang yaitu (C0), cluster tinggi yaitu (C1), dan cluster rendah yaitu (C2). Perhitungan pada Rapidminer mengungkapkan bahwa Kota Tangerang dan Kota Tangerang Selatan berada di cluster 0, Kabupaten Pandeglang, Kab Lebak, Kab Serang masuk dalam cluster 1, serta Kabupaten Tangerang, Kota Cilegon, Kota Serang berada di cluster 2.

Identifikasi Kebangkrutan Perusahaan Menggunakan Algoritma Regresi Linear Berganda Haryadi, Deny; Rahman Hakim, Arif; Marini Umi Atmaja, Dewi; Basri, Amat; Adisty Nilasari, Risma
Tech-E Vol. 6 No. 2 (2023): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

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

Corporate bankruptcy can hurt the company and affect the state of the economy. Therefore, many interested parties want to know the business situation related to the company. These parties include creditors, auditors, shareholders, and management itself who have an interest in knowing the state of the company in the context of bankruptcy. The past financial statements of a company can be used to predict future financial conditions using report analysis techniques. In the risk assessment process, expert knowledge is still seen as an important task, because expert predictions are subjective. This study aims to predict the bankruptcy of the company using influencing factors such as the level of research and development costs, the growth rate of total assets, and the current asset turnover rate. The method used in this research is the prediction method using the Linear Regression Algorithm. Based on the test results show that the variables or attributes used in this study have a significant effect, as evidenced by using a linear regression algorithm to be able to produce a Root Mean Squared Error value: 0.162 +/- 0.000.

Analisis Retorika Visual dan Sentimen Analisis pada Kampanye Ganjar Pranowo di dalam Video Adzan Magribh Rahmayadi, Gagas Ezhar; Haryadi, Deny; Danniswara, R. Bhima; Surjadi, Valentino Kenny; Wendra, Netanel Danur
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
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.v9i2.3557

Campaigns are a form of communication used by politicians to gain support. According to theory, campaigns are persuasive communications aimed at changing the behavior of the communicant as the message recipient. The choice of media in conducting campaigns is an important factor. The role of media will strengthen the communication concept so that the message conveyed can be interpreted according to the goal. In the 2024 presidential campaign, one of the presidential candidates, Ganjar Pranowo, used television advertisements, specifically the Maghrib call to prayer, as part of his campaign strategy. The method used in this research employs a qualitative approach. Visual Rhetoric Theory is used to interpret the message in the video. Additionally, supporting sentiment analysis theory is used to analyze the research subject, namely the public who have seen the video. Public responses were taken from social media comments. Thus, the results of the analysis from both theories are combined into one conclusion. The result is that Ganjar Pranowo made a fairly good impression, but it is not perfect. There are still some errors in the video that raise questions about Ganjar Pranowo's credibility.

Analisis Retorika Visual dan Sentimen Analisis pada Kampanye Ganjar Pranowo di dalam Video Adzan Magribh Rahmayadi, Gagas Ezhar; Haryadi, Deny; Danniswara, R. Bhima; Surjadi, Valentino Kenny; Wendra, Netanel Danur
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
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.v9i2.3557

Campaigns are a form of communication used by politicians to gain support. According to theory, campaigns are persuasive communications aimed at changing the behavior of the communicant as the message recipient. The choice of media in conducting campaigns is an important factor. The role of media will strengthen the communication concept so that the message conveyed can be interpreted according to the goal. In the 2024 presidential campaign, one of the presidential candidates, Ganjar Pranowo, used television advertisements, specifically the Maghrib call to prayer, as part of his campaign strategy. The method used in this research employs a qualitative approach. Visual Rhetoric Theory is used to interpret the message in the video. Additionally, supporting sentiment analysis theory is used to analyze the research subject, namely the public who have seen the video. Public responses were taken from social media comments. Thus, the results of the analysis from both theories are combined into one conclusion. The result is that Ganjar Pranowo made a fairly good impression, but it is not perfect. There are still some errors in the video that raise questions about Ganjar Pranowo's credibility.

Advancing Vehicle Logo Detection with DETR to Handle Small Logos and Low-Quality Images Ubaidillah, Rifky Fahrizal; Sulistiyo, Mahmud Dwi; Kosala, Gamma; Rachmawati, Ema; Haryadi, Deny
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6236

Image-based vehicle logo detection is an important component in the implementation of vehicle information recognition technology, which supports the development of intelligent transportation systems. Vehicle logos, as elements that represent the identities of vehicle brands and models, play a significant role in completing vehicle identity data. The information obtained from this logo can be utilized to solve various traffic problems, such as vehicle document counterfeiting and theft, and for better traffic planning and management purposes. However, the main challenge in developing an accurate logo detection system lies in the wide variety of shapes, sizes, and positions of logos in different types of vehicles. In addition, the generally small size of logos, especially on certain vehicles, often makes it difficult for computer-based detection systems to recognize logos consistently, thus affecting the overall performance of the detection model. In this research, the Detection Transformers (DETR) method is used to build a vehicle logo detection system that focuses on small-scale logo. The testing process was conducted using the VL-10 dataset, which was specifically designed for vehicle logo detection evaluation. The results show that the DETR model can detect vehicle logos very well, even for small-scale logos. The model achieved an AP50 value of 0.952, which indicates a high level of accuracy and reliability in detecting the vehicle logo in the dataset used.

Implementation of Support Vector Regression for Polkadot Cryptocurrency Price Prediction Haryadi, Deny; Hakim, Arif Rahman; Atmaja, Dewi Marini Umi; Yutia, Syifa Nurgaida
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.945

Cryptocurrency investment is an investment instrument that has high risk but also has a greater advantage than other investment instruments. To make a big profit, investors need to analyze cryptocurrency investments to predict the price of the cryptocurrency to be purchased. The highly volatile movement of cryptocurrency prices makes it difficult for investors to predict those prices. Data mining is the process of extracting large amounts of information from data by collecting, using data, the history of data relationship patterns, and relationships in large data sets. Support Vector Regression has the advantage of doing accurate cryptocurrency price predictions and can overcome the problem of overfitting by itself. Polkadot is one of the cryptocurrencies that are often used as investment instruments in the world of cryptocurrencies. Polkadot cryptocurrency price prediction analysis using the Support Vector Regression algorithm has a good predictive accuracy value, including for Polkadot daily closing price data, namely with a radial basis function (RBF) kernel with cost parameters C = 1000 and gamma = 0.001 obtained model accuracy of 90.00% and MAPE of 5.28 while for linear kernels with parameters C = 10 obtained an accuracy of 87.68% with a MAPE value of 6.10. It can be concluded that through parameter tuning, the model formed has an accuracy value and the best MAPE is to use a radial kernel basis function (RBF) with cost parameters C = 1000 and gamma = 0.001. The results show that the Support Vector Regression method is quite good if used for the prediction of Polkadot cryptocurrencies.

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