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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) E-Jurnal Matematika Indonesian Journal on Computing (Indo-JC) JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Visualita : Jurnal Online Desain Komunikasi Visual Journal of Data Science and Its Applications BERNAS: Jurnal Pengabdian Kepada Masyarakat eProceedings of Engineering Jurnal INFOTEL
Annisa Aditsania
Universitas Telkom
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Prediksi Penyebaran Penyakit Demam Berdarah Dengue (DBD) di Kabupaten Bandung menggunakan Hybrid Random Forest (RF) dan Genetic Algorithm (GA) Maharani Padma Utami; Fhira Nhita; Annisa Aditsania
eProceedings of Engineering Vol 6, No 2 (2019): Agustus 2019
Publisher : eProceedings of Engineering

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Abstract

Penyakit Demam Berdarah Dengue (DBD) merupakan penyakit yang ditularkan melalui nyamuk Aedes Ageypti. Nyamuk tersebut berada di wilayah tropis dan sub tropis seperti Asia Tenggara. Di Indonesia, khususnya Kabupaten Bandung jumlah kasus penyakit ini cenderung meningkat dan daerah penyebarannya semakin luas. Oleh karena itu, diperlukan informasi mengenai penyebaran untuk membantu menganalisa dalam menentukan tindakan yang harus dilakukan untuk menanganinya pada masa yang akan datang. Dalam menyusun informasi ini, dilakukan pengolahan data penyinaran lama matahari, kelembapan, angin, temperature, uap, curah hujan, jumlah kasus, jumlah penduduk dan presentase PHBS di Kabupaten Bandung menggunakan metode Hybrid Random Forest dan Genetic Algorithm untuk menemukan prediksi penyebaran penyakit yang hasilnya ditampilkan dengan tools yaitu GIS (Geographic Information System). Perbandingan menggunakan algoritma Random Forest tanpa Genetic Algorithm dan Random Forest dengan Genetic Algorithm, adalah akurasi rata-rata sebesar 83,42% dan 89,12 %. Kata kunci : prediksi, penyebaran, demam berdarah dengue, Random Forest, Genetic Algorithm
Implementation of Ant Colony Optimization – Artificial Neural Network in Predicting the Activity of Indenopyrazole Derivative as Anti-Cancer Agent Isman Kurniawan; Nabilla Kamil; Annisa Aditsania; Erwin Budi Setiawan
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1055

Abstract

Cancer is a disease induced by the abnormal growth of cells in body tissues. This disease is commonly treated by chemotherapy. However, at first, cancer cells can respond to the activity of chemotherapy over time, but over time, resistance to cancer cells appears. Therefore, it is required to develop new anti-cancer drugs. Indenopyrazole and its derivative have been investigated to be a potential drug to treat cancer. This study aims to predict indenopyrazole derivative compounds as anti-cancer drugs by using Ant Colony Optimization (ACO) and Artificial Neural Network (ANN) methods. We used 93 compounds of indenopyrazole derivative with a total of 1876 descriptors. Then, the descriptors were reduced by using the Pearson Correlation Coefficient (PCC) and followed by the ACO algorithm to get the most relevant features. We found that the best number of descriptors obtained from ACO is ten descriptors. The ANN prediction model was developed with three architectures, which are different in hidden layer number, i.e., 1, 2, and 3 hidden layers. Based on the results, we found that the model with three hidden layers gives the best performance, with the value of the R2 test, R2 train, and Q2 train being 0.8822, 0.8495, and 0.8472, respectively.
Prediksi Harga Mata Uang Kripto Menggunakan LSTM dan MLR Samuel Erlangga; Indwiarti Indwiarti; Annisa Aditsania
eProceedings of Engineering Vol 10, No 3 (2023): Juni 2023
Publisher : eProceedings of Engineering

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Abstract

Abstrak-Perkembangan mata uang kripto terjadi begitu pesat dalam beberapa tahun ini. Hal ini membuat orang-orang semakin tertarik untuk menginvestasikan uangnya ke dalam mata uang kripto. Namun untuk mempelajari pergerakan pasar dan ilmu untuk memprediksi harga menggunakan analisa teknikal tidaklah mudah, seringkali terjadinya perubahan yang begitu besar pada harga membuat mata uang kripto menjadi aset yang cukup diminati namun beresiko tinggi. Tidak jarang para investor mengalami kerugian dikarenakan hasil analisa yang kurang tepat dan karena transaksi jual beli mata uang kripto yang tidak dipatok oleh waktu memungkinkan harga berubah kapan saja. Pada penelitian ini melakukan prediksi harga mata uang kripto berupa harga penutupan mata uang kripto tersebut. Prediksi mata uang kripto ini dilakukan dengan metode Long Short-Term Memory (LSTM) dan Multiple Linear Regression (MLR). Dengan menggunakan histori harga tutup sebelumnya (Close) dan jumlah penambahan kasus positif Covid-19 untuk menjadi data latih. Menggunakan dua mata uang kripto untuk memastikan mesin dapat bekerja untuk lebih dari satu mata uang kripto. Hasil dari kedua metode ini adalah prediksi harga mata uang kripto yang dibandingkan dengan menghitung nilai Root Mean Squared Error (RMSE) untuk memastikan metode apa yang lebih baik digunakan dalam memprediksi harga mata uang kripto. Hasil terbaik dihasilkan oleh model LSTM dengan nilai RMSE 0.000240.Kata kunci-LSTM, MLR, mata uang kripto 
Discrete Wavelet Transform (DWT) dan Random Forest untuk Deteksi Kanker Berdasarkan Klasifikasi Data Microarray Monica Triyani; Adiwijaya Adiwijaya; Annisa Aditsania
JURNAL INFOTEL Vol 12 No 3 (2020): August 2020
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v12i3.484

Abstract

Cancer is one of the leading causes of death worldwide. According to the World Health Organization (WHO), in 2018, about 9.6 million deaths caused by cancer. DNA microarray technology has played an important role in analyzing and diagnosing cancer. The accuracy resulting from the classification of Random Forests is not optimal because microarrays have large dimensional data. Therefore, it is necessary to reduce the dimensions of the Discrete Wavelet Transform (DWT) as a feature to reduce dimensions and increase accuracy in microarray data. Based on the simulation, the dimension can be reduced and improve the accuracy of classification up to 8% - 20%. DWT approximation coefficient can improve accuracy better than detailed coefficients for data on colon cancer 100%, lung cancer 100%, ovarian 100%, prostate tumor 80%, and central nervous system 83.33%.
Fitur Seleksi pada Data Microarray untuk Deteksi Kanker Berdasarkan Klasifikasi Random Forest Tita Nurul Nuklianggraita; Adiwijaya Adiwijaya; Annisa Aditsania
JURNAL INFOTEL Vol 12 No 3 (2020): August 2020
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v12i3.485

Abstract

Cancer is a disease that can affect all organs of humans. Based on data from the World Health Organization (WHO) fact sheet in 2018, cancer deaths have reached 9.6 million. One known way to detect cancer that is with Microarray Technique, but the microarray data have large dimensions due to the number of features that are very much compared to the number of samples. Therefore, dimension reduction should be made to produce optimum accuracy. In this paper, we compare Minimum Redundancy Maximum Relevance (MRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) to reduce the dimension of microarray data. Moreover, by using Random Forest (RF) Classifier, the performance of classification (cancer detection) is compared. Based on the simulation, it can be concluded that LASSO is better than MRMR because it can produce an evaluation of 100% in lung and ovarian cancer, 92% colon cancer, 93% prostate tumor, and 83% central nervous system.
Implementation of Ant Colony Optimization – Artificial Neural Network in Predicting the Activity of Indenopyrazole Derivative as Anti-Cancer Agent Isman Kurniawan; Nabilla Kamil; Annisa Aditsania; Erwin Budi Setiawan
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1055

Abstract

Cancer is a disease induced by the abnormal growth of cells in body tissues. This disease is commonly treated by chemotherapy. However, at first, cancer cells can respond to the activity of chemotherapy over time, but over time, resistance to cancer cells appears. Therefore, it is required to develop new anti-cancer drugs. Indenopyrazole and its derivative have been investigated to be a potential drug to treat cancer. This study aims to predict indenopyrazole derivative compounds as anti-cancer drugs by using Ant Colony Optimization (ACO) and Artificial Neural Network (ANN) methods. We used 93 compounds of indenopyrazole derivative with a total of 1876 descriptors. Then, the descriptors were reduced by using the Pearson Correlation Coefficient (PCC) and followed by the ACO algorithm to get the most relevant features. We found that the best number of descriptors obtained from ACO is ten descriptors. The ANN prediction model was developed with three architectures, which are different in hidden layer number, i.e., 1, 2, and 3 hidden layers. Based on the results, we found that the model with three hidden layers gives the best performance, with the value of the R2 test, R2 train, and Q2 train being 0.8822, 0.8495, and 0.8472, respectively.
Peningkatan Pemahaman Masyarakat Terhadap Nilai Cagar Budaya Berbasis Wisata Tematik Google Maps di Purwakarta Ratri Wulandari; Vika Haristianti; Idhar Resmadi; Djoko Murdowo; Annisa Aditsania; Aida Andrianawati; Rendy Pandita B; Wibisono Tegar GP; Aniq Atiqi R; Siti Saadah
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jb.v4i1.4018

Abstract

Pada era Hindia Belanda, Kabupaten Purwakarta adalah ibukota Karesidenan Karawang, sehingga di Kabupaten Purwakarta terdapat kawasan pusat kota dengan alun-alun dan kelengkapan infrastruktur lainnya. Kelebihan ini belum disadari sebagai potensi oleh pemerintah daerah maupun masyarakat. Padahal, pemahaman terhadap potensi cagar budaya akan mendorong peningkatan Indeks Pembangunan Kebudayaan (IPK) daerah. Dari permasalahan tersebut, kegiatan pengabdian masyarakat ini menawarkan dua solusi yaitu, memberikan ilmu dan metode untuk peningkatan pemahaman masyarakat terhadap potensi cagar budaya daerah dan pengembangan peta wisata tematik berbasis Google Maps. Di dalamnya terdapat kegiatan terkait inventarisasi dan dokumentasi bangunan cagar budaya, termasuk pengetahuan teknologi untuk membangun media literasi cagar budaya. Metode yang digunakan melalui survey lapangan dan wawancara, penyusunan proposal, dan pencarian alternative solusi. Pada pelaksanaan kegiatan, metode yang digunakan adalah transfer pengetahuan melalui kegiatan workshop Adapun luaran kegiatan berupa infografis, peta tematik cagar budaya, dan website. Serta transfer pengetahuan dan metode kepada staf DISPORAPARBUD, dan masyarakat pecinta warisan budaya di bawah binaan DISPORAPARBUD Kabupaten.
Co-Authors A Rakha Ahmad Taufiq Aditya Iftikar Riaddy Adiwijaya Adrin, Athaya Fatharani Aisyah Aisyah Aldi Riyadi Ramadhan Amalya Citra Pradana Ananda Affan Fattahila Andhika Rama Putra ANDI FUTRI HAFSAH MUNZIR Andrianawati, Aida Aniq Atiqi R Aniq Atiqi Rohmawati Arfian Nurdiansyah Arnanti Primiana Yuniati Choiryaldi Setya Pratama Danang Triantoro Murdiansyah Danu Ardiyanto Deni Saepudin Djoko Murdowo EKA ANDHIKA KURNIAWAN Erwin Budi Setiawan Fadhlika Hadi Faturachman Nugraha Sasmita Fendi Irfan Amorokhman Fhira Nhita I Gde Made Bagus Nurseta Wijaya Idhar Resmadi Indwiarti Isman Kurniawan Iyon Priyono Jondri Jondri Kaenova Mahendra Auditama Kaisa Sekaring Pertiwi Lazuardy Azhari Bacharuddin Noor M Ikhsan Perdana Putra Maharani Padma Utami Mahmud Imrona Mela Mai Anggraini Monica Triyani Muhamad Paisal Hanip Muhammad Arief Priambodo Muhammad Fikrie Abdillah Muhammad Wildan Putra Aldi Nabilla Kamil Nuklianggraita, Tita Nurul Oki Virgiawan Pramudita Oscar Ramadhan P. H. GUNAWAN Prabasworo, Bhanu Putri Tsatsabila Ramadhani Putu Harry Gunawan Ratri Wulandari Redi Nurjamin Rendy Pandita B Rian Febrian Umbara Rian Putra Mantovani Rifaldi Rizqi Pratama Rizki Amanullah Hakim Roizal Manullang Samuel Erlangga Siti Saadah Sri Soedewi Theo Andrew Theresya Margharetha Panjaitan Tita Nurul Nuklianggraita Triyani, Monica Ulky Parulian Wibowo UMMU SALAMAH Untari Novia Wisesty Vika Haristianti Wibisono Tegar GP Yuliant Sibaroni