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All Journal Jurnal Mantik JUSTIN (Jurnal Sistem dan Teknologi Informasi) Tanjungpura International Journal on Dynamics Economics, Social Sciences, and Agribusiness (TIJDESSA) Jurnal Inovasi Global Jurnal Aplikasi dan Riset Informatika
Septiriana, Rina
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Astronomy Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences

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Case Based Reasoning Diagnosis Hama Pada Tanaman Kelapa Sawit Septiriana, Rina; Tursina, Tursina; Yuliarni, Nella
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 2 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i2.72590

Abstract

Di Indonesia, kelapa sawit adalah salah satu komoditas pertanian utama dalam memberikan dukungan signifikan terhadap perekonomian, hal ini disebabkan karena tanaman kelapa sawit memiliki nilai ekonomi yang cukup tinggi. Namun Budidaya tanaman kelapa sawit rentan terhadap serangan hama yang dapat mengancam hasil panen dan produktivitas yang dapat disebabkan juga karena kurangnya pengetahuan petani dalam mengelola kebun dan mengatasi masalah tersebut.  Untuk mengatasi permasalahan tersebut pada penelitian ini akan membangun aplikasi Case-Based Reasoning (CBR) yang membantu dalam mendiagnosis hama pada tanaman kelapa sawit dengan algoritma nearest neighbor. CBR akan menyelesaikan permasalahan baru dengan memanfaatkan kembali permasalahan lama yang memiliki kesamaan yang telah memiliki solusi sebelumnya. Algoritma nearest neighbor berguna untuk menghitung similaritas antar kasus baru dengan kasus yang berada di basis kasus.  Data untuk penelitian  melibatkan 7 jenis hama, 10 gejala, dan 95 kasus serangan hama pada kelapa sawit. Pengujian aplikasi menggunakan 15 kasus uji serta 80 basis kasus dari hasil pengujian aplikasi menunjukkan hasil akurasi sebesar 37,5%, dengan nilai similaritas tertinggi mencapai 0,95.  Meskipun  aplikasi mampu memberikan solusi yang efektif, terdapat keterbatasan dalam penentuan kasus terpilih ketika memiliki nilai similaritas yang sama.
Comparison of the nearest neighbor algorithm and C4.5 for the retrieval on case-based reasoning process (case study: children respiratory disorders) Tursina, Tursina; Septiriana, Rina
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5018

Abstract

The diagnosis of respiratory problems was usually made through direct consultation with a pediatric respiratory specialist or by studying several previous respiratory disorders cases. These cases were gleaned from prior experiences or the knowledge of subject-matter experts. Case-Based Reasoning (CBR) is the processing of diagnosing a patient based on past cases or expertise. Retrieve, reuse, revise, and retain are some of the steps of case-based reasoning. The retrieval stage of CBR was where the classification method searches for similarity values. Numerous algorithms exist for classification techniques, such as C4.5 and the Nearest Neighbour algorithm. This study compares the similarities between the C4.5 and Nearest Neighbor algorithms. The Nearest Neighbour approach was used to search for similarity, and the results show that 99.33% of the items classified based on learning data were nearest to the object. By contrast, the accuracy value for the C4.5 approach was 100%.
The Implementation of Quizizz in Learning at SDN 08 Sui Ruk Bengkayang Nasution, Helfi; Sujaini, Herry; Septiriana, Rina; Muthahari, Morteza; Hafidh, Khairul
Tanjungpura International Journal on Dynamics Economics, Social Sciences and Agribusiness Vol. 4 No. 1 (2023): Tanjungpura International Journal On Dynamics Economic, Social Sciences and Agr
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/tijdessa.v4i1.39

Abstract

The development of Information and Computer Technology (ICT) has had a broad impact on various sectors of life, including the education sector and ICT mastery. Through formal education, students are equipped with insight from an early age which becomes the basis for continuous learning and developing problem-solving skills. However, in Bengkayang District, West Kalimantan, the ICT education program has not yet reached students in several rural schools, including SDN 08 Sungai Ruk, where students are not familiar with computers both in terms of hardware and software. In addition, the COVID-19 pandemic has had a significant impact on education. Since March 16 2020 in Indonesia, students have been studying from home through an online system to prevent the spread of the virus. Nonetheless, the government continues to encourage teaching and learning activities even though remotely. Based on these problems, Quizizz is implemented for students of SDN 08 Sungai Ruk so that elementary school students have knowledge and skills in operating computers both in terms of hardware and software, and increase learning motivation. This implementation provides an introduction to computer introduction material regarding computer basics such as computer components and their functions. Followed by a Fun Game that aims to break the ice. And finally an interactive quiz was held using the Quizizz platform which was based on the material previously presented.
Comparison of Support vector machine and Naïve Bayes Classification Algorithms Using VADER and Lexicon based Labelling on Indonesian and English Tweets Sunarko, Ponco; Putra Negara, Arif Bijaksana; Septiriana, Rina
Jurnal Aplikasi dan Riset Informatika Vol 3, No 1 (2024)
Publisher : Jurnal Aplikasi dan Riset Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/juara.v3i1.86468

Abstract

Sentiment analysis is essential in natural language processing, and it helps understand public opinion from text, especially on social media. This research compares the effectiveness of Naive Bayes and Support vector machine (SVM) algorithms in sentiment classification of automatically labelled tweets using VADER and Lexicon-based methods. The data consists of Indonesian and English tweets collected through scrapping. The methodology includes business understanding, data understanding, data preparation, modelling, evaluation, and deployment stages. In the preprocessing stage, the data is cleaned and divided into 300 sentences for test data in Indonesian and English; each data will be labelled manually, and then 3762 sentences for Indonesian data and 4308 sentences for English data will be used as training data. The highest accuracy on automatic labelling against manual labelling is on Lexicon-based labelling, showing 66% accuracy for Indonesian and 55% for English. Text features were extracted using TF-IDF, and the model was trained and tested with the labelled data. The results showed that SVM with Lexicon-based auto-labelling had the best performance, with an accuracy of 44% for Indonesian and 57% for English. The combined accuracy of automatic labelling and classification was 29% for Indonesian and 31% for English. Factors such as tweet length, dictionary limitations, and use of slang affected the accuracy. The analysis also showed biases in the data and auto-labelling results.
Perbandingan Metode Moving Average Dalam Memprediksi Pemakaian Air PDAM Tirta Khatulistiwa Tursina, Tursina; Septiriana, Rina; Lestari, Marlinda Puji
Jurnal Inovasi Global Vol. 3 No. 5 (2025): Jurnal Inovasi Global
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jig.v3i5.332

Abstract

Air menjadi salah satu kebutuhan yang sangat penting bagi masyarakat dalam kehidupan sehari-hari. Namun pemakaian air dan permintaan air konsumen pada PDAM Tirta Khatulistiwa tidak selalu stabil setiap waktunya. Pemakaian air dapat diprediksi menggunakan metode Moving Average. Dalam Moving Average terdapat beberapa pendekatan diantaranya adalah metode Simple Moving Average, Double Moving Average, Weighted Moving Average, dan Exponential Moving Average. Penelitian ini membandingkan keempat metode tersebut untuk mengetahui hasil keakuratan dengan menggunakan metode Mean Forecase Error (MFE). Data yang digunakan dalam penelitian yaitu data bulan Januari 2016 sampai dengan bulan Maret 2020, terdapat 3 percobaan periode yaitu 2 periode, 3 periode, dan 4 periode pada keempat metode. Berdasarkan jumlah periode dengan 51 data metode Weighted Moving Average mempunyai nilai yang optimal hasil prediksinya dengan nilai MFE sebesar 14649, dibandingkan metode Simple Moving Average, Double Moving Average, dan Exponential Moving Average. Sedangkan jumlah periode dengan 24 data metode Simple Moving Average mempunyai nilai yang optimal hasil prediksinya dengan nilai MFE sebesar 375, dibandingkan metode Double Moving Average, Weighted Moving Average, dan Exponential Moving Average.
Co-Authors Arif Bijaksana Putra Negara Helfi Nasution Herry Sujaini Khairul Hafidh Lestari, Marlinda Puji Muthahari, Morteza Sunarko, Ponco Tursina Tursina Yuliarni, Nella