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All Journal Jurnal Ilmu Komputer dan Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) JMM (Jurnal Masyarakat Mandiri) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI JISKa (Jurnal Informatika Sunan Kalijaga) Scientific Journal of Informatics Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) SinarFe7 Jurnal ELTEK Jurnal Informatika: Jurnal Pengembangan IT

Khadijah Fahmi Hayati Holle

Universitas Islam Negeri Maulana Malik Ibrahim Malang

Author-ID : 4162746

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Library & Information Science Other

Published : 19 Documents Claim Missing Document

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Diagnosis Penyakit Jantung Menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) Holle, Khadijah Fahmi Hayati
MATICS Vol 8, No 2 (2016): MATICS
Publisher : Department of Informatics Engineering

The number of uncertain risk factor in heart disease makes experts difficult to diagnose its disease. Computer technology in the health field is mostly used. In this paper, we implement a system to diagnose heart disease. The used method is Adaptive neuro-fuzzy inference system which combine the advantage of fuzzy and neural network. The used data is UCI Cleveland data that have 13 attributes as inputs. Output system diagnosis compared with observational data for evaluation. System performance tested by calculating accuracy. Tests were also conducted on the variation of the learning rate, iteration, minimum error, and the use of membership functions. Accuracy obtained from test is 65,657% where using membership function Beta.

Sistem Automatic Text Summarization Menggunakan Algoritma Textrank Zamzam, Muhammad Adib; Crysdian, Cahyo; Hayati Holle, Khadijah Fahmi
MATICS Vol 12, No 2 (2020): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v12i2.8372

Text summarization (perangkuman teks) adalah pendekatan yang bisa digunakan untuk meringkas atau memadatkan teks artikel yang panjang menjadi lebih pendek dan ringkas sehingga hasil rangkuman teks yang relatif lebih pendek bisa mewakilkan teks yang panjang. Automatic Text Summarization adalah perangkuman teks yang dilakukan secara otomatis oleh komputer. Terdapat dua macam algoritma Automatic Text Summarization yaitu Extraction-based summarizationÂ dan Abstractive summarization. Algoritma TextRank merupakan algoritma extraction-based atau extractive, dimana ekstraksi di sini berarti memilih unit teks (kalimat, segmen-segmen kalimat, paragraf atau passages), lalu dianggap berisi informasi penting dari dokumen dan menyusun unit-unit (kalimat-kalimat) tersebut dengan cara yang benar. Hasil penelitian dengan input 50 artikel dan hasil rangkuman sebanyak 12,5% dari teks asli menunjukkan bahwa sistem memiliki nilai recall ROUGE 41,659 %. Nilai tertinggi recall ROUGE tertinggi tercatat pada artikel 48 dengan nilai 0,764. Nilai terendah recall ROUGE tercatat pada artikel Â 37 dengan nilai 0,167.

PREFERENCE BASED TERM WEIGHTING FOR ARABIC FIQH DOCUMENT RANKING Khadijah Fahmi Hayati Holle; Agus Zainal Arifin; Diana Purwitasari
Jurnal Ilmu Komputer dan Informasi Vol 8, No 1 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

In document retrieval, besides the suitability of query with search results, there is also a subjective user assessment that is expected to be a deciding factor in document ranking. This preference aspect is referred at the fiqh document searching. People tend to prefer on certain fiqh methodology without rejecting other fiqh methodologies. It is necessary to investigate preference factor in addition to the relevance factor in the document ranking. Therefore, this research proposed a method of term weighting based on preference to rank documents according to user preference. The proposed method is also combined with term weighting based on documents index and books index so it sees relevance and preference aspect. The proposed method is Inverse Preference Frequency with Î± value (IPFÎ±). In this method, we calculate preference value by IPF term weighting. Then, the preference values of terms that is equal with the query are multiplied by Î±. IPFÎ± combined with the existing weighting methods become TF.IDF.IBF.IPFÎ±. Experiment of the proposed method uses dataset of several Arabic fiqh documents. Evaluation uses recall, precision, and f-measure calculations. Proposed term weighting method is obtained to rank the document in the right order according to user preference. It is shown from the result with recall value reach 75%, precision 100%, and f-measure 85.7% respectively.

MITIGASI COVID 19 MELALUI PELATIHAN MEMBUAT HAND SANITIZER TAKMIR MASJID SEBAGAI UPAYA MINIMALISASI PENYEBARAN VIRUS CORONA DI KLASTER TEMPAT IBADAH Ulfi Andrian Sari; Hayyun Lathifaty Yasri; Khadijah Fahmi Hayati Holle
JMM (Jurnal Masyarakat Mandiri) Vol 5, No 5 (2021): Oktober
Publisher : Universitas Muhammadiyah Mataram

Abstrak: Di era new normal, kebutuhan hand sanitizer penting untuk mencegah penyebaran virus corona, sehingga perlu diadakan pelatihan pembuatan hand sanitizer sesuai standar WHO. Tujuan pengabdian ini adalah memberikan pelatihan kepada takmir masjid untuk membuat hand sanitizer secara mandiri. Metode pengabdian menggunakan Participatory Action Research (PAR) yang terdiri dari plan, action dan refleksi. Peserta pelatihan adalah takmir masjid di Kelurahan Purwantoro sebanyak 50 orang. Hasil dari pengabdian ini adalah 1) perencanaan dilakukan dengan pihak kelurahan Purwantoro dan kepala rukun warga (RW) 5 terlaksana dengan baik. 2) Tahap action terlaksana dengan baik. Hasil pengamatan yang dilakukan saat praktik membuat hand sanitizer sebanyak 75% takmir masjid mampu membuat hand sanitizer. 3) Refleksi dilakukan dengan pendistribusian hand sanitizer dan masker ke masjid-masjid di Purwantoro.Abstract: In the new normal era, the need for Hand Sanitizers to prevent the spread of the COVID-19 pandemic is urgently needed, so training on making hand sanitizers is needed according to WHO standards. The purpose of this service is to provide training to the manager’s mosque to make hand sanitizers independently. The service method uses Participatory Action Research (PAR) which consists of plan, action and reflection. The training participants are manager’s mosque in Purwantoro Village as many as 50 people. The results of this service are 1) the planning carried out with the Purwantoro village and the head of the community unit (RW) 5 is carried out well. 2) The action stage was carried out well. The results of observations made during the practice of making hand sanitizers were 75% of manager’s mosque were able to make hand sanitizers. 3) Reflection is done by distributing hand sanitizers and masks to mosques in Purwantoro.

Air Quality Forecasting in DKI Jakarta Using Artificial Neural Network Asfilia Nova Anggraini; Nisa Kholifatul Ummah; Yessy Fatmasari; Khadijah Fahmi Hayati Holle
MATICS Vol 14, No 1 (2022): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v14i1.13863

The increase in the use of motorized vehicles increases air pollution conditions, especially in big cities such as the capital city of Indonesia, Jakarta. The pollution that pollutes this city contains various kinds of chemical particles that are harmful to living things when they enter the body. several efforts to reduce this pollution have been carried out, one of which is by identifying the pollutants contained in the air. This study uses data obtained from monitoring stations to predict the content of pollutants in the air at some time in the future. the method used is data mining forecasting with a neural network model. by using rapid miner obtained several graphic descriptions of pollutant conditions in Jakarta that go up and down. pollutant levels of SO2, CO, PM10 and NO2 all increased in the November-December period and at the same time period, ozone was at its lowest point. Results from Prediction air quality using Artificial Neural Network with 5 parameters, shown on this pollutant PM10 had an RMSE of 9,477; SO2 had an RMSE 5,474; CO had an RMSE 8,392; O3 had an RMSE 18,250; NO2 had an RMSE 5,171. Can be concluded that the RMSE value of O3 is higher than the others and the lowest value of NO2.

Analisis Sentimen Terhadap Permendikbud Ristek Nomor 30 Tahun 2021 pada Media Sosia Twitter Menggunakan Metode Lexicon-Based dan Multinomial Naïve Bayes Kurniyatul Ainiyah; Khadijah Fahmi Hayati Holle
Jurnal Ilmiah Informatika Vol. 7 No. 1 (2022): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v7i1.29-40

Regulation of the Minister of Education, Culture, Research, and Technology (Permendikbud Ristek) Number 30 of 2021 was launched as a form of government efforts in the context of preventing and handling sexual violence in universities. However, it turns out that this regulation has generated various reactions from the community, most of them support it while others reject the ratification of this regulation. Technological developments that occur today encourage people to write their opinions on social media, one of which is Twitter. Tweets discussing this rule can be used to gauge public sentiment. However, considering the number of tweets, the classification process will be difficult to do manually, so it requires a computational system that can automatically classify the sentiments of the existing tweets. From these problems, a system is designed to perform sentiment analysis using the lexicon-based method and Multinomial Naïve Bayes. The results of this sentiment measurement can be useful as data analysis material for the Ministry of Education and Culture, Research and Technology in making decisions regarding this rule. The purpose of this research is to measure the value of accuracy, precision, recall, and f-measure in sentiment analysis using lexicon-based and Multinomial Naïve Bayes methods. The measurement results obtained using a dataset of 470 data are the accuracy value of 71.28%, precision of 70.10%, recall of 78%, and f-measure value of 74.29%.

Perbandingan Metode Klasifikasi Data Mining Untuk Deteksi Keaslian Lowongan Pekerjaan di Medsos Mohammad Malik Fajar; Annisa Rizkiana Putri; Khadijah Fahmi Hayati Holle
Jurnal Ilmiah Informatika Vol. 7 No. 1 (2022): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v7i1.41-48

The COVID-19 pandemic has resulted in more and more people losing their jobs. Due to layoffs or bankrupt companies. This has resulted in many people looking for job vacancies. Job vacancies are circulating on social media but there are real and fake ones. Irresponsible people create job vacancies on social media with fraudulent purposes or for personal gain. So, a comparison of data mining classification methods was made for the detection of authenticity of job vacancies on social media. The method used is naive bayes, KNN, and decision tree. In order to find out which method has the highest accuracy value and can be used to classify the authenticity of job vacancies, and fraud on social media can be prevented. Based on this research, the method that has the highest accuracy value is the KNN method. The accuracy value is 94.93%, while the Decision Tree model has an accuracy value of 91.57% and the Naive Bayes model has an accuracy of 84.35%. The KNN method is the best method for classifying the authenticity of job vacancies.

IMPLEMENTASI TEKNIK KRIPTOGRAFI RSA UNTUK PENGAMANAN DATA PENGIRIMAN SMS Ainafatul Nur Muslikah; Hardiana Riski Riswanto; Khamaida Safinah; Khadijah Fahmi Hayati Holle
Jurnal Ilmiah Informatika Vol. 5 No. 1 (2020): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v5i1.749

Message sending is one activity that is often used by everyone. However, security in this message delivery system needs to be wary of spying or message piracy during the process of sending messages. Surely someone who sent the message does not know if someone's personal message has been stolen. With this initiative builds a security message using cryptographic RSA algorithm where the message sender or recipient of the message can send the message safely without being known to the message hijacker or spy. Cryptography that uses the RSA algorithm to secure messages. This RSA algorithm message will be decrypted with the public key and to encrypt the message. This application was built on the Android platform because the dominant person has an Android smartphone with a system that runs the length of the message character does not affect the speed at the time of sending the message to the recipient, and there is no limit on the length of the message character during the encryption process so that any length of the massage character can be encrypted well.

Smart Assessment menggunakan Backpropagation Neural Network Agung Teguh Wibowo Almais; Cahyo Crysdian; Khadijah Fahmi Hayati Holle; Akbar Roihan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 21 No 3 (2022)
Publisher : LPPM Universitas Bumigora

Penerapan scraping dan Backpropagation Neural Network dapat menjadikan penilaian Self- Assessment Questionnaire (SAQ) website Pemerintah Daerah Provinsi Jawa Timur lebih smart jika dibandingkan dengan model assessment yang sudah ada. Langkah awal yaitu melakukan scraping website Pemerintah Daerah Provinsi Jawa Timur untuk mendapatkan nilai SAQ. Hasil scraping tersebut akan digunakan sebagai data uji pada metode Backpropagation Neural Network, kemudian hasil data uji akan di proses menggunakan 4 jenis model data yang berbeda-beda dari segi jumlah iterasi dan hidden layer untuk mendapatkan akurasi terbaik. Pada model data A menggunakan iterasi 1000 dan 5 hidden layer menghasilkan nilai Mean Squared Error (MSE) 0,0117, Mean Absolute Percent Error (MAPE) 39,36% dan Akurasi 60.64%. Model data B menggunakan iterasi 1000 dan 7 hidden layer menghasilkan nilai MSE 0,0087, MAPE 29,49% dan Akurasi 70,50%. Model data C dengan menggunakan iterasi 2000 dan 9 hidden layer menghasilkan nilai MSE 0,0064, MAPE 24,46% dan Akurasi 75,53%. Model data D menggunakan iterasi 2000 dan 9 hidden layer menghasilkan nilai MSE 0,0036, MAPE 18,71% dan Akurasi 81,28%. Dari hasil ujicoba tersebut bahwa model data D yang menggunakan iterasi 2000 dan 9 hidden layer menghasilkan tingkat akurasi yang terbaik sehingga model data D dapat dijadikan acuan hasil penilaian website Pemerintah Daerah Provinsi Jawa Timur tahun 2021.

Analisis Perbandingan Algoritma Decision Tree, kNN, dan Naive Bayes untuk Prediksi Kesuksesan Start-up Adhitya Prayoga Permana; Kurniyatul Ainiyah; Khadijah Fahmi Hayati Holle
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 6 No. 3 (2021): September 2021
Publisher : UIN Sunan Kalijaga Yogyakarta

Start-ups have a very important role in economic growth, the existence of a start-up can open up many new jobs. However, not all start-ups that are developing can become successful start-ups. This is because start-ups have a high failure rate, data shows that 75% of start-ups fail in their development. Therefore, it is important to classify the successful and failed start-ups, so that later it can be used to see the factors that most influence start-up success, and can also predict the success of a start-up. Among the many classifications in data mining, the Decision Tree, kNN, and Naïve Bayes algorithms are the algorithms that the authors chose to classify the 923 start-up data records that were previously obtained. The test results using cross-validation and T-test show that the Decision Tree Algorithm is the most appropriate algorithm for classifying in this case study. This is evidenced by the accuracy value obtained from the Decision Tree algorithm, which is greater than other algorithms, which is 79.29%, while the kNN algorithm has an accuracy value of 66.69%, and Naive Bayes is 64.21%.

Co-Authors Adhitya Prayoga Permana Agung Teguh Almais Agung Teguh Wibowo Almais Agus Zainal Arifin Ainafatul Nur Muslikah Akbar Roihan Alfianita, Rizha Allin Junikhah Annisa Rizkiana Putri Asfilia Nova Anggraini Aziza, Miladina Rizka Cahyo Crysdian Diana Purwitasari Ekaputri, Enggarani Wahyu Fatchurrohman Fatchurrohman Hardiana Riski RIswanto Hayyun Lathifaty Yasri Junikhah, Allin Khamaida Safinah Kurniyatul Ainiyah Kurniyatul Ainiyah Mohammad Malik Fajar Munna, Daurin Nabilatul Mustofa, Ahmad Habibil Nadila Sugianti Nisa Kholifatul Ummah Novrindah Alvi Hasanah Putri, Hikmatul Maulidia Romadhoni, Yuliana Salma Fatia Ulfi Andrian Sari Wibowo, Muhammad Ismail Arjun Yayang Galuh Yessy Fatmasari Yuliana Romadhoni Yunifa Miftachul Arif Yusuf Ansori Zamzam, Muhammad Adib

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