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All Journal Sistemasi: Jurnal Sistem Informasi Seminar Nasional Lahan Suboptimal Jurnal Mnemonic Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen
Athalina, Ghita
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Other

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Artificial Intelligence dalam Pengendalian Hama dan Penyakit Tanaman Herlinda, Siti; Nursalim, Yossi Aprian; Anggraini, Erise; Athalina, Ghita
Seminar Nasional Lahan Suboptimal Vol 12, No 1 (2024): Vol 12, No 1 (2024): Prosiding Seminar Nasional Lahan Suboptimal ke-12 “Revital
Publisher : Pusat Unggulan Riset Pengembangan Lahan Suboptimal (PUR-PLSO) Universitas Sriwijaya

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Abstract

Herlinda, S., Nursalim, Y.A.,  Anggraini, E., &Athalina, G. (2024). Artificial intelligence in pest and disease management. In: Herlinda S et al. (Eds.), Prosiding Seminar Nasional Lahan Suboptimal ke-12 Tahun 2024, Palembang  21 Oktober 2024. (pp. 27–47).  Palembang: Penerbit & Percetakan Universitas Sriwijaya (UNSRI).The article reviews the developments of artificial intelligence (AI) in the control of pests and diseases in agriculture.  Artificial intelligence refers to the ability of digital computers or computer-controlled robots to do activities typically associated with human intelligence by emulating cognitive functions. At present, artificial intelligence is employed across various sectors, including healthcare, education, and agriculture. In agriculture, AI has been used as a mechanism for pest and disease management in plants. AI offers advantages due to its labor-saving efficiency, targeted effectiveness, and sustainability, as it ensures safety for users, the environment, and the items manufactured.  Robots, in conjunction with sensors, satellites, and drones, can precisely detect symptoms and coloration of diseased plants and those infested by pests. This AI can precisely identify an appropriate methods to control plant pests and diseases. AI can facilitate routine processes in integrated pest management, such as monitoring ecosystems (biotic and abiotic factors) and determining the right timing and methods for control, thereby achieving sustainable pest management.
IMPLEMENTASI ALGORITMA RANDOM FOREST REGRESSION DALAM PREDIKSI HARGA LAPTOP Septiyanah, Siska; Athalina, Ghita
Jurnal Mnemonic Vol 8 No 1 (2025): Mnemonic Vol. 8 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v8i1.12475

Abstract

Penggunaan laptop telah menjadi kebutuhan signifikan dalam kehidupan modern, baik untuk keperluan pribadi, bisnis, maupun pendidikan. Pasar laptop terus berkembang pesat setiap tahunnya, didorong oleh kemajuan teknologi yang menghasilkan beragam jenis laptop dengan spesifikasi dan harga yang bervariasi. Prediksi harga laptop menjadi tantangan penting dalam analisis data, terutama dengan semakin beragamnya spesifikasi dan merek di pasar. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang mempengaruhi harga laptop dan mengimplementasikan algoritma Random Forest Regression untuk melakukan prediksi harga secara akurat. Dataset yang digunakan mencakup berbagai atribut seperti RAM, CPU, GPU, resolusi layar, jenis laptop (TypeName), ukuran memori, dan berat laptop. Hasil analisis menunjukkan bahwa atribut RAM, CPU, GPU, dan resolusi layar memiliki pengaruh signifikan terhadap harga laptop, sementara TypeName dan ukuran layar (inches) tidak memberikan kontribusi yang berarti. Namun, ukuran memori dan berat laptop menjadi faktor penting yang dipertimbangkan. Model Random Forest Regression memberikan performa prediksi yang baik dengan nilai akurasi (R-squared) sebesar 0,83
Optimalisasi Kinerja Karyawan Berbasis HR Analytics dengan K-Means Clustering dan Analisis Faktor Demografi Ramadhani, Anandita Nabilla; Athalina, Ghita
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.779

Abstract

Data-driven Human Resources (HR) management is an important aspect in improving organizational productivity and efficiency in the digital era. This research aims to cluster employees based on performance using the K-Means Clustering algorithm and evaluate the influence of demographic factors on job performance. The dataset used is a public dataset from Kaggle, including employee performance information such as Key Performance Indicators (KPIs), training scores, multiple trainings, performance appraisals, awards, as well as demographic attributes such as gender, age, education level, and recruitment channels. Using the six-stage CRISP-DM framework, the data was processed using StandardScaler, and the optimal number of clusters was determined through the Elbow Method, Davies-Bouldin Index, and Silhouette Score, resulting in two main clusters. Cluster 0 includes high-performing employees with KPIs above 80%, good performance ratings, and good training scores, while Cluster 1 consists of low-performing employees, with lower KPIs, poor performance ratings, and training scores. Analysis showed demographic factors did not significantly affect employee performance. This research recommends focused training for low-performing employees and rewards for high-performing employees, so that each employee can reach their full potential and support organizational productivity.
Comparison of Naive Bayes and SVM Algorithms for Sentiment Analysis of PUBG Mobile on Google Play Store Sari, Putri Ratna; Indah, Dwi Rosa; Rasywir, Errissya; firdaus, Mgs Afriyan; Athalina, Ghita
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4814

Abstract

PlayerUnknown's Battlegrounds (PUBG) Mobile is one of the most popular mobile games in Indonesia, according to data from the Google Play Store. According to the Google Play Store, the game has a rating of 3.8 with 49.5 million reviews. While a considerable number of users express satisfaction, a significant proportion of reviews also contain criticism regarding the gameplay and features. However, a cursory examination of reviews may not fully capture the nuances of user sentiment, necessitating a more comprehensive sentiment analysis. This research will employ a positive and negative sentiment analysis of Indonesian PUBG Mobile reviews on the Google Play Store, utilizing a comparative approach to evaluate the performance of two algorithms: Naïve Bayes and Support Vector Machine (SVM). The data set comprised 2,000 user reviews, which were collected using a scraping technique. Following this, a labeling process was conducted based on the rating, data were preprocessed, TF-IDF weighting was applied, and both algorithms were implemented. The findings indicated that users expressed satisfaction with the game's visuals and gameplay. However, there were also technical concerns that required attention, including bugs, server instability, lag, and performance issues. The SVM algorithm demonstrated superior performance, with an accuracy rate of 70.95%, compared to Naïve Bayes, which reached 69.83%. Despite Naïve Bayes's faster processing speed, SVM exhibited greater precision, recall, and F1-score
Co-Authors Dwi Rosa Indah Erise Anggraini, Erise Errissya Rasywir Mgs Afriyan Firdaus Nursalim, Yossi Aprian Putri Ratna Sari, Putri Ratna Ramadhani, Anandita Nabilla Septiyanah, Siska SITI HERLINDA