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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Simetris Jurnal Edukasi dan Penelitian Informatika (JEPIN) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Applied Data Sciences
Annisa Annisa
IPB University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Spatial decision tree model for garlic land suitability evaluation Andi Nurkholis; Imas Sukaesih Sitanggang; Annisa Annisa; Sobir Sobir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp666-675

Abstract

Predicting land and weather characteristics as indicators of land suitability is very important in increasing effectiveness in food production. This study aims to evaluate the suitability of garlic land using spatial decision tree algorithm. The algorithm is the improvement of the conventional decision tree algorithm in which spatial join relation is included to grow up spatial decision tree. The spatial dataset consists of a target layer that represents garlic land suitability and ten explanatory layers that represent land and weather characteristics in the study areas of Magetan and Solok district, Indonesia. This study generated the best spatial decision trees for each study area. On Magetan dataset, the best model has 33 rules with 94.34% accuracy and relief variable as the root node, whereas on Solok dataset, the best model has 66 rules with 60.29% accuracy and soil texture variable as the root node.
SKYLINE QUERY BASED ON USER PREFERENCES IN CELLULAR ENVIRONMENTS Ruhul Amin; Taufik Djatna; Annisa Annisa; Imas Sukaesih Sitanggang
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4192

Abstract

The recommendation system is an important tool for providing personalized suggestions to users about products or services. However, previous research on individual recommendation systems using skyline queries has not considered the dynamic personal preferences of users. Therefore, this study aims to develop an individual recommendation model based on the current individual preferences and user location in a mobile environment. We propose an RFM (Recency, Frequency, Monetary) score-based algorithm to predict the current individual preferences of users. This research utilizes the skyline query method to recommend local cuisine that aligns with the individual preferences of users. The attributes used in selecting suitable local cuisine include individual preferences, price, and distance between the user and the local cuisine seller. The proposed algorithm has been implemented in the JALITA mobile-based Indonesian local cuisine recommendation system. The results effectively recommend local cuisine that matches the dynamic individual preferences and location of users. Based on the implementation results, individual recommendations are provided to mobile users anytime and anywhere they are located. In this study, three skyline objects are generated: soto betawi (C5), Mie Aceh Daging Goreng (C4), and Gado-gado betawi (C3), which are recommended local cuisine based on the current individual preferences (U1) and user location (L1). The implementation results are exemplified for one user located at (U1L1), providing recommendations for soto betawi (C5) with an individual preference score of 0.96, Mie Aceh Daging Goreng (C4) with an individual preference score of 0.93, and Gado-gado betawi (C3) with an individual preference score of 0.98. Thus, this research contributes to the field of individual recommendation systems by considering the dynamic user location and preferences.
Perbandingan Kinerja LSTM dan Prophet untuk Prediksi Deret Waktu (Studi Kasus Produksi Susu Sapi Harian) Alusyanti Primawati; Imas Sukaesih Sitanggang; Annisa Annisa; Dewi Apri Astuti
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 3 (2023): Volume 9 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i3.72031

Abstract

Prediksi deret waktu dibutuhkan untuk menjawab pertanyaan bisnis dimasa depan  yang akurat sehingga perlunya membangun model prediksi yang memiliki kinerja bagus. Pendekatan machine learning seperti long short term memory (LSTM) dan Prophet menjadi popular saat ini untuk pemodelan prediksi deret waktu. Agribisnis susu segar saat ini salah satu studi kasus yang memerlukan peranan teknologi informasi seperti bisnis intelijen untuk memastikan ketersediaan pasokan susu dimasa depan. Upaya pertama yang perlu dilakukan adalah menyiapkan model prediksi yang tepat meskipun data awal yang dikumpulkan masih sedikit atau terbatas. Dataset produksi susu sapi selama 300 hari menjadi data penelitian yang dimodelkan kedalam LSTM dan Prophet. Keduanya dibandingkan kinerjanya terhadapa data terbatas. Hasilnya uji koefisien determinasi R2 keduanya yaitu 0.2, sehingga perlu dilakukan peningkatan kinerja melalui tahapan revise and enhance. Hasilnya, kedua model meningkat nilai R2 menjadi 0.3 dan LSTM lebih baik dari Prophet. Meskipun demikian perbedaan keduanya tidak terlalu signifikan dan peningkatan juga tidak berbeda terlalu jauh karena data susu memiliki pola multi-periode dengan tren berbeda signifikan. Periode 90 hari pertama adalah masa klimaks laktasi sedangkan periode kedua setelah 90 hari adalah masa intervensi peternak menurunkan hasil perah untuk persiapakan ternak kambing perah ke masa kawin dan bunting.
Model Klasifikasi Lahan Hijaun Pakan Ternak Ruminansia Dengan Algoritma Random Forest Pada Kabupaten Lumajang Dwi Marlina; Imas Sukaesih Sitanggang; Annisa Annisa; Dewi Apri Astuti
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.15967

Abstract

Informasi mengenai ketersediaan lahan hijauan pakan ternak ruminansia pada tutupan lahan memerlukan data spasial yang akurat, salah satunya dapat diperoleh melalui teknologi penginderaan jauh. Citra satelit Landsat 8 mampu menyediakan informasi mengenai tutupan lahan, termasuk lahan hijauan, badan air, pemukiman, industri, dan jalan. Citra satelit tidak hanya menginformasikan lahan hijauan saja tetapi dapat menginformasikan tutupan lahan seperti badan air, pemukinan, industri, dan jalan. Oleh karena itu, diperlukan proses klasifikasi tutupan lahan untuk mengindentifikasi area yang berfungsi sebagai sumber hijauan pakan ternak ruminansia. Identifikasi ini penting untuk mengetahui ketersediaan pakan, yang selanjutnya dapat digunakan sebagai dasar dalam memprediksi biomassa vegetasi. Penelitian ini bertujuan untuk mengklasifikasi tutupan lahan hijauan yang berperan sebagai pakan ternak ruminansia. Metode yang digunakan adalah algoritma random forest dengan memanfaatkan citra satelit Landsat 8 untuk wilayah , Kabupaten Lumajang pada periode tahun 2018 hingga 2022. Hasil klasifikasi menghasilkan tiga kelas utama lahan hijaua, yaitu perkebunan, pertanian/sawah, dan semak belukar. Model klasifikasi yang dibangun mencapai tingkat akurasi sebesai 93%. Berdasarkan hasil analisis, rat-rata lahan hijauan di Kabupaten Lumajang terdiri atas lahan perkebunan sebuas 23.865,78 ha, pertanian/sawah seluas 18.363,21 ha, dan semak belukar seluas 949,98 ha. Hasil penelitian menunjukkan bahwa lahan hijauan di Kabupaten Lumajang didominasi oleh perkebunan, sehingga daerah ini memiliki potensi yang baik untuk pengembangan hijauan sebagai pakan ternak ruminansia. Ketersediaan lahan yang luas diharapkan dapat mendukung usaha peternakan dan pengelolaan sumber daya pakan di wilayah tersebut.
Development of Skyline Query Algorithm for Individual Preference Recommendation in Streaming Data Ruhul Amin; Taufik Djatna; Annisa Annisa; Sukaesih Sitanggang
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.599

Abstract

The ability of a recommendation system to deliver relevant outcomes is significantly influenced by its adaptability to the dynamic nature of individual user preferences. Data-streaming-based recommendation systems face substantial challenges in aligning recommendations with rapid shifts in user preferences. Previous research on the development of skyline query algorithms has predominantly focused on processing efficiency and parallel performance optimization yet has not addressed the dynamic nature of individual user preferences—an essential factor for generating relevant and responsive recommendations in streaming data environments. This study aims to develop a skyline query algorithm called Distributed Data Skyline (DDSky) to provide recommendations based on dynamic individual user preferences within data-streaming contexts. DDSky leverages the Recency, Frequency, Monetary, and Rating (RFMRT) model to capture real-time changes in user preferences. This model is integrated with parallel skyline computation and structured to enhance the data processing efficiency on a large scale. The parallel processing approach divides tasks into smaller subtasks executed simultaneously across multiple threads. This strategy enables the simultaneous processing of attributes such as price, distance, and individual user preferences, thereby delivering relevant and responsive recommendations to real-time changes in user preferences. The DDSky algorithm was evaluated using a local dataset from the JALITA application and compared with the Eager algorithm. The results demonstrated that DDSky outperformed Eager, achieving an average recall value of 0.45 and an F1-measure of 0.55, compared to Eager's recall value of 0.33 and F1-measure of 0.47. Furthermore, DDSky achieved an average precision of 0.73, which closely approached Eager's precision of 0.82. Additionally, DDSky exhibited optimal throughput performance for datasets containing up to 10,000 items with high flexibility across various data types. With its unique technical approach, DDSky delivers more responsive and relevant recommendations to dynamic user preferences, establishing its superiority in data-streaming-based recommendation systems.
Co-Authors Alusyanti Primawati Andi Nurkholis DEWI APRI ASTUTI Dwi Marlina Ruhul Amin Ruhul Amin Sitanggang, Imas S. Sobir Sobir Sukaesih Sitanggang Taufik Djatna