AGENTS: Journal of Artificial Intelligence and Data Science

SISTEM PENDUKUNG KEPUTUSAN REKOMENDASI PEMBERIAN BANTUAN SOSIAL DENGAN MULTI OBJECTIVE OPTIMIZATION BY RATIO ANALYSIS (MOORA)

Zulhisham Ramli

Bantuan Langsung Tunai (BLT) merupakan salah satu program bantuan bersyarat dari pemerintah sebagai bentuk program penanggulangan kemiskinan yang meliputi perlindungan sosial, jaminan sosial, pemberdayaan sosial, rehabilitasi sosial, dan pelayanan dasar. Pada tahap seleksi calon penerima (BLT) Desa Lamatti Riaja di Kabupaten Sinjai belum sepenuhnya tepat sasaran karena masih dilakukan secara manual sehingga banyak yang belum tepat sasaran. Berdasarkan hal ini maka dilakukan penelitian untuk merancang sebuah sistem pendukung keputusan untuk mempermudah dalam pengecekan data warga yang berhak menerima dana BLT setiap penyalurannya. Sehingga proses seleksi lebih objektif, efisien waktu dan dapat meminimalisir kesalahan seleksi yang mungkin timbul dalam pemilihan calon penerima BLT.

Pada penelitian ini digunakan metode Multi Objective Optimization By Ratio Analysis (MOORA). Data sampling yang gunakan adalah data hasil observasi Masyarakat di Desa Lamatti Riaja. Proses perhitungan menggunakan algoritma MOORA dan implementasi sistem berbentuk Website dengan metode perancangan sistem yang digunakan adalah System Development Life Cycle memberikan hasil yang baik dan akurat. Sedangkan metode pengujian yang digunakan adalah Black Box.

Penelitian ini menghasilkan suatu website Sistem Pengambilan Keputusan (SPK) dengan menggunakan implementasi subsistem pengelolaan data menggunakan MySQL. Hasil simulasi perhitungan data penerima BLT dengan menggunakan algoritma MOORA meminimalisir kesalahan seleksi yang mungkin timbul dalam pemilihan calon penerima BLT.

Optimization of Student Graduation Predictions on Time Using Binning And Synthetic Minority Oversampling Technique (SMOTE)

Faidhul Rahman, Mustikasari Mustikasari — Sat, 30 Mar 2024 00:00:00 +0000

On-time student graduation is a situation where a student graduates from their educational program at the time planned or determined by the relevant educational institution. This research aims to optimize predictions of student graduation on time using the Binning method to group variables into discrete categories and Synthetic Minority Oversampling Technique (SMOTE) to overcome class imbalances in the dataset. Data containing several variables was analyzed using the Naïve Bayes, Decision Tree and Random Forest machine learning algorithms. Model evaluation is carried out using metrics such as precision, Recall, accuracy, and F1-score. The results confirm that the combination of Binning and SMOTE has a significant impact on increasing prediction accuracy. It is hoped that the results of this research can contribute to increasing the accuracy of predicting student graduation on time. By optimizing the use of Binning and SMOTE, it is hoped that the prediction model can overcome the problem of data imbalance and provide more accurate information to higher education institutions to take the necessary preventive actions to increase student graduation rates and become a reference for similar research in the future.

Public Sentiment Analysis Towards the Implementation of 2024 Election Preparations Using the Maximum Entropy Method

Indra Ismawan Indra Ismawan, Mustikasari Mustikasari, Wahyuddin Saputra — Sat, 30 Mar 2024 00:00:00 +0000

The General Election Commission (KPU) and the Election Supervisory Agency (Bawaslu) are tasked with coordinating and overseeing elections throughout the territory of Indonesia. Nevertheless, elections are not without various disputes, including violations of ethical codes and administrative issues. General elections are an important way for the public to exercise their political participation rights. In the era of information technology, the public is increasingly active in expressing their opinions and sentiments through social media such as Twitter. Utilizing data from social media, the maximum entropy method is employed to classify opinions found in tweets based on entropy values. This research also aims to measure the accuracy level of the maximum entropy method in sentiment classification. From the results of this study, using approximately 1,400 data, an accuracy rate of 87.08% was obtained, which is quite good and highly feasible for further development to achieve better results

Sentiment Analysis Of Online Loan Application Reviews Using Support Vector Machine (SVM) Method

ST. Aminah Dinayati Ghani, Nur Salman, Farhan Wahyuta Kusuma — Sat, 02 Mar 2024 00:00:00 +0000

Online loans, abbreviated as "Pinjol," refer to the practice of lending money online through applications or websites without involving traditional financial institutions such as banks or other traditional creditors. Examples of applications in this field include AkuLaku and Kredivo. These apps operate in the e-commerce and credit provision sectors in Southeast Asia, including Indonesia. Despite their operational strengths, these applications have both advantages and disadvantages, leading to positive and negative reviews on platforms like the Play Store. The research aims to identify positive and negative reviews within these online loan applications that can influence users' decisions when choosing a particular app. SVM classification technique is employed to analyze positive and negative sentiments from these reviews. The accuracy results obtained after sentiment analysis for Kredivo are 81%, while for AkuLaku, it is 75%. A higher accuracy value indicates a better ability of the model to predict sentiments correctly. Visualization of impactful words based on word frequency is presented in the form of a Word Cloud. Therefore, based on the sentiment analysis conducted using the SVM model, the author suggests choosing the Kredivo app when selecting an online loan application, as the analysis indicates that Kredivo has better quality compared to AkuLaku.

Implementation of K-Means Clustering Algorithm in Food Sales (Case Study: Ayam Betutu at Warung Wardana)

Audi mayori, Yuyun Tresnawati — Sun, 25 Feb 2024 00:00:00 +0000

Ayam Betutu Warung Wardana is one of
the small food and beverage businesses which is in great demand to relax or to do work. To maximize
this business, managers must know the needs of customers who have visited, therefore they can
improve services according to what is needed. Besides that, there are problems that often occur,
namely problems regarding the menu that has the most potential to be improved and the menu that is
most in demand by consumers. From the problems that have been described above, this study applies
the K-means algorithm clustering method to determine consumer interest in a food and beverage
menu at Ayam Betutu Warung Wardana. In this study, the authors used a framework based on the
Cross Industry Standard Process for Data Mining (CRISP-DM) method. This study produced the best
two clusters with the Silhoutte and DBI validation methods. Testing using the Silhoutte Coefficient
shows a value of 0.44 and it is the best value close to the Davies Bouldin Index (DBI) showing the
value with the smallest ratio at 0.9030707. This is good because the ratio is close to zero, the better
the K-Means grouping.