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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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Cancer Detection based on Microarray Data Classification Using Principal Component Analysis and Functional Link Neural Network Iyon Priyono; Adiwijaya Adiwijaya; Annisa Aditsania
Journal of Data Science and Its Applications Vol 3 No 2 (2020): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2020.3.52

Abstract

Cancer is a deadly disease caused by abnormal growth of tissue cells that are not controlled in the body. In 2018, according to Globocan data, the number of cancer sufferers has increased from the previous years which was 18.1 million people, with a mortality rate of 9.6 million. In recent years, cancer prediction using DNA microarrays data can help medical experts in analyzing whether a person has cancer or not. DNA microarray data have very large and complex gene expression, therefore a dimensional reduction method is needed. Then, the dimension reduction results will be used for classification into types of cancer or not. In this paper, Principal Component Analysis (PCA) is used as a feature extraction to reduce dimension and Functional Link Neural Network as a classifier. Based on the simulation, the average of accuracy using the FLNN and PCA about 76.08%. Keywords: cancer detection, Microarray data, Functional Link Neural Network, Principal Component Analysis.
QSAR Study on Aromatic Disulfide Compounds as SARS-CoV Mpro Inhibitor Using Genetic Algorithm-Support Vector Machine Rizki Amanullah Hakim; Annisa Aditsania; Isman Kurniawan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 2, May 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i2.1428

Abstract

COVID-19 is a type of pneumonia caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus causes severe acute respiratory syndrome and 2 million active cases of COVID-19 have been found worldwide. A new strain of the SARS-CoV-2 virus emerged that proved to be more virulent than its predecessor. Regarding the design of a new inhibitor for this strain, SARS-CoV Main Protease (Mpro) was used as the target inhibitor. In the in silico development, the Quantitative Structure-Activity Relationship (QSAR) method is commonly used to predict the biological activity of unknown compounds to improve the process of drug design of a disease, including COVID-19. In this study, we aim to develop a QSAR model to predict the activity of aromatic disulfide compounds as SARS-CoV Mpro inhibitors using Genetic Algorithm (GA) – Support Vector Machine (SVM). GA was used for feature selection, while SVM was used for model prediction. The used dataset is set of features of aromatic disulfide compounds, along with information on the toxicity activity. We found that the best SVM model was obtained through the implementation of the polynomial kernel with the value of R2­­train and R2test­ scores are 0.952 and 0.676, respectively.
Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying Public Opinion about COVID-19 Vaccine in Indonesia Siti Saadah; Kaenova Mahendra Auditama; Ananda Affan Fattahila; Fendi Irfan Amorokhman; Annisa Aditsania; Aniq Atiqi Rohmawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (557.978 KB) | DOI: 10.29207/resti.v6i4.4215

Abstract

COVID-19 was classified as a pandemic in March 2020, and then in July 2021, this virus had its variance that spreads all over the world including Indonesia. The probability of the detrimental of its effect cannot be avoided, because this virus has a huge transmission risk during daily activity. To prevent suffering from COVID-19, people certainly need to be vaccinated. In responding to its vaccine, the citizen of Indonesia become expressive, so they try to express opinions, for example by uploading text on Twitter. Those expressions can be learned using deep learning frameworks which are BERT, CNN-LSTM, and IndoBERTweet to get knowledge about negative speech categories such as anxiety, panic, and emotion, or positive speech such as vaccines whether worked well. By then, these three methods accomplish in carrying out the prediction of sentiments about vaccination using dataset tweets on Twitter from January-2021 to March-2022, for instance using IndoBERT succeeds to classify sentiments as positive sentiment at around 80%, and then IndoBERTweet at 68%, in addition using CNN-LSTM reach 53% with the total of using 2020 dataset from Twitter. According to these results, a lesson learned for continued improvement for Indonesia's Government or authorities can be acquired in ending the COVID-19 pandemic.
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 | Full PDF (546.609 KB) | 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.
Implementation of Ensemble Methods on Classification of CDK2 Inhibitor as Anti-Cancer Agent Isman Kurniawan; Mela Mai Anggraini; Annisa Aditsania; Erwin Budi Setiawan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 1 (2023): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.78537

Abstract

Cancer is known as the second leading cause of death worldwide. About 7-10 million cases of death by cancer occur every year. The recent treatment to heal the cancer is chemotherapy. However, chemotherapy treatment is known to have side effects and cell resistance issues to certain drugs. Therefore, it is required to develop a new drug that can reduce the side effects and provide a better treatment effect. In general, anti-cancer drugs are developed by targeting Cyclin-Dependent Kinase 2 (CDK2) enzyme. Conventional drug design is not effective and efficient for obtaining new drug candidates because of no information about the biological activity before it is synthesized. In this study, we aim to develop a model to predict the activity of CDK2 inhibitors by using ensemble methods, i.e.,  XGBoost, Random Forest, and AdaBoost. The study was conducted by calculating several fingerprints, i.e., Estate, Extended, Maccs, and Pubchem, as feature variables. Based on the results, we found that Random Forest with Pubchem fingerprint gives the best result with the value of Matthews Correlation Coefficient (MCC) and Area Under the ROC Curve (AUC) values are 0.979 and 0.999, respectively. From this study, we contributed to revealing the potency of the ensemble with fingerprint in bioactivity prediction, especially CDK2 inhibitors as anti-cancer agents.
Analisis Dan Implementasi Long Short Term Memory Neural Network Untuk Prediksi Harga Bitcoin Muhammad Wildan Putra Aldi; Jondri Jondri; Annisa Aditsania
eProceedings of Engineering Vol 5, No 2 (2018): Agustus 2018
Publisher : eProceedings of Engineering

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Abstract

Abstrak Bitcoin adalah salah satu cryptocurrency yang sedang diminati untuk menjadi media investasi dalam meraih keuntungan secara finansial. Meskipun sedang digemari, investasi menggunakan bitcoin masih memiliki resiko yang cukup besar. Agar dapat mengantisipasi resiko yang didapat dalam berinvestasi menggunakan Bitcoin, diperlukan suatu sistem prediksi yang dapat memprediksi pergerakan kurs nilai tukar Bitcoin. Untuk memprediksi harga Bitcoin maka data historis harga Bitcoin akan dipelajari hingga mengenali polapola tertentu. Jaringan syaraf tiruan merupakan salah satu metode yang memiliki kemampuan untuk mempelajari polapola dari suatu data. Sistem yang dibangun pada penelitian ini adalah menggunakan metode jaringan syaraf tiruan yaitu dengan menggunakan arsitektur Long Short Term Memory Neural Networks. Namun teknik ini memerlukan parameter yang tepat untuk mendapatkan hasil prediksi yang akurat. Dalam tugas akhir ini menganalisis beberapa parameter seperti jumlah pola time series, jumlah neuron hidden, max epoch, dan komposisi data latih dan uji terhadap akurasi prediksi yang didapatkan. Hasil analisis menunjukkan bahwa sistem yang dibangun mampu memprediksi harga Bitcoin dengan baik, dengan rata-rata tingkat akurasi sebesar 93.5% terhadap data testing. Kata kunci : bitcoin, prediksi, jaringan syaraf tiruan, long short term memory Abstract Bitcoin is one of the cryptocurrencies that are in demand to become a medium of investment in achieving financial benefits. While it is popular, investments using bitcoin still have considerable risk. In order to anticipate the risks involved in investing using Bitcoin, a prediction system is needed that can predict the movement of the Bitcoin exchange rate. To predict the price of Bitcoin, Bitcoin price historical data will be studied to recognize certain patterns. Artificial neural networks are one method that has the ability to study patterns of data. The system built on this research is using artificial neural network method by using Long Short Term Memory Neural Networks architecture. But this technique needs the right parameters to get accurate prediction results. In this final project analyze the number of neurons in the input and hidden layer to the prediction accuracy obtained. The results of the analysis show that the built system is able to predict the Bitcoin price well, with an accuracy of 95.12% to the data testing. Keywords: bitcoin, prediction, artificial neural network, long short term memory
Pelacakan Keluaran Sistem Linear Pompa Piston Tunggal Dengan Kontrol Panjang Batang Penghubung Pelampung Dan Piston Danu Ardiyanto; Jondri Jondri; Annisa Aditsania
eProceedings of Engineering Vol 3, No 2 (2016): Agustus, 2016
Publisher : eProceedings of Engineering

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Abstract

Tujuan dari penelitian ini adalah membangun ulang model sistem persamaan pompa piston dan merancang kontrol pompa piston terhadap panjang batang penghubung antara pelampung dan pompa piston. Siste m persamaan yang digunakan adalah sistem persamaan linear dan metode yang digunakan untuk menyelesaikan sistem persamaan linear ini adalah Runge-Kutta Orde 4. Fokus pada penelitian ini adalah untuk merancang panjang batang penghubung antara pelampung dan piston agar menghasilkan perubahan tekanan pada pompa piston. Diharapkan panjang batang penghubung antara pelampung dan piston dapat digunakan sebagai variabel kontrol untuk sistem persamaan pompa piston tunggal. Perancangan panjang batang penghubung antara pelampung dan piston dilakukan menggunakan derajat relative dan tracking persamaan linear. Hasil yang didapatkan dari persamaan yang sudah menggunakan variabel kontrol panjang batang penghubung antara pelampung dan piston diperoleh nilai panjang batang penghubung antara pelampung dan piston sebesar 137,6928 m hingga 143,6112 m. Kata Kunci : pompa piston tunggal, sistem persamaan linear, tracking sistem persamaan, Runge-Kutta
Pelacakan Keluaran Sistem Linear Pompa Piston Tunggal Dengan Kontrol Massa Redi Nurjamin; Jondri Nasri; Annisa Aditsania
eProceedings of Engineering Vol 3, No 2 (2016): Agustus, 2016
Publisher : eProceedings of Engineering

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Abstract

Untuk memanfaatkan gelombang laut menjadi energi listrik, dibutuhkan pompa piston. Pompa piston yang ditinjau pada penelitian ini adalah pompa piston tunggal. Pompa piston tunggal ini terdiri dari empat elemen yang bergerak didalam sistem yang terdapat didalam pompa piston tunggal yaitu pelampung, batang, piston dan air dipompa. Pelampung berfungsi sebagai objek yang disimpan dipermukaan air laut yang mana pelampung akan mengikuti gerak dari gelombang laut yang akan membantu piston untuk bergerak secara naik turun yang dihubungkan oleh batang (rod). Piston yang terdapat didalam silinder berguna untuk memompa air yang ada didalam pompa piston untuk menghasilkan perbedaan tekanan yang dapat dikonversi menjadi energi listrik. Tujuan dari Tugas Akhir ini adalah untuk merekontruksi sistem persamaan dari pompa piston tunggal dan merancang kondisi optimal dari massa piston sebagai kontrol terhadap nilai tekanan didalam reservoir yang monoton naik sehingga nilai tekanan didalam reservoir dapat terkontrol di titik tertentu. Metode yang digunakan untuk mencari solusi sistem kontrol dari persamaan pompa piston tunggal adalah metode Runge-Kutta 4 dan tracking kontrol sistem persamaan linear. Kata kunci : piston, Runge-Kutta, derajat relatif
Penjadwalan Sidang Tugas Akhir Prodi Ilmu Komputasi Universitas Telkom Menggunakan Metode Algoritma Genetika Adaptif Dan Fuzzy Relation Oki Virgiawan Pramudita; Fhira Nhita; Annisa Aditsania
eProceedings of Engineering Vol 3, No 2 (2016): Agustus, 2016
Publisher : eProceedings of Engineering

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Abstract

Determine a schedule that consist of many elements are complicated. One example of scheduling problems is scheduling of final project. Lot of combinations can be formed from the elemensts of this scheduling, but not all such combinations may be the solution. The scheduling of final project have requirements such lecturers teaching schedule and college student course schedule should not be conflicting to hearing schedule, examiners recommended in accordance with the topic of thesis that are tested and adviser should not be a examiners. Considering of many consideration in this scheduling, then it takes the optimal and efficient method so it can resolve the complexity of this scheduling. Fuzzy relations and genetic algorithm including methods that can be used to overcome this problem. Schedule that produced by fuzzy relation and genetic algorithm methods in this case have an average fitness of 0.9687, and for genetic algorithm methods only have an average fitness of 0.8243. Keywords: Scheduling, fuzzy relation, adaptive genetic algorithm, genetic algorithm.
Penentuan Fitur Supervised Learning Dalam Identifikasi Kalimat Sitasi Pada Makalah Ilmiah Rian Putra Mantovani; Yuliant Sibaroni; Annisa Aditsania
eProceedings of Engineering Vol 3, No 1 (2016): April, 2016
Publisher : eProceedings of Engineering

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Abstract

Kalimat sitasi berperan penting dalam penulisan jurnal ilmiah. Kalimat sitasi dapat diidentifikasi dengan mengekstraksi fiturnya. Pada penelitian ini digunakan 5 fitur utama dan juga akan dikombinasikan. Fitur-fitur yang kita gunakan adalah unigram, bigram, proper noun, cue phrase, dan pronoun. Untuk mengklasifikasi kita menggunakan Naive Bayes (NB) dan support vector machine (SVM). Penelitian ini menggunakan 500 makalah ilmiah yang diambil dari acl-arc. Hasil dari penelitian ini adalah fitur yang terbaik untuk mengidentifikasi kalimat sitasi adalah “Proper Noun, dan Cue Phrase” dengan 59,069% f-measure, dan 92,157% akurasi, jika menggunakan naive bayes, dan 51,234% f-measure, dan 92,503% akurasi jika menggunakan SVM. Kata Kunci —supervised learning, ekstraksi fitur, identifikasi, kalimat sitasi
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