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All Journal Jurnal Informatika Jurnal Pilar Nusa Mandiri Jurnal Sains dan Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Informatik : Jurnal Ilmu Komputer Jurnal Riset Informatika INTI Nusa Mandiri Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Teknik Informatika C.I.T. Medicom Jurnal Pengabdian Masyarakat Bangsa

M. Rangga Ramadhan Saelan

Universitas Nusa Mandiri

Author-ID : 5818730

Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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DETEKSI PENYAKIT ALZHEIMER MENGGUNAKAN ALGORITMA NAÃVE BAYES DAN CORRELATION BASED FEATURE SELECTION Wildah, Siti Khotimatul; Agustiani, Sarifah; S, M. Rangga Ramadhan; Gata, Windu; Nawawi, Hendri Mahmud
Jurnal Informatika Vol 7, No 2 (2020): September 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Alzheimer merupakan kelainan berupa penimbunan plak atau protein tidak normal dalam otak sehingga menyebabkan hilangnya sel neuron dan menjadi salah satu pemicu penyakit demensia yang dapat mengakibatkan terhambatnya aktivitas sehari-hari karena penurunan daya ingat,kesulitan dalam berkomunikasi, tidak dapat berpikir jernih, terjadinya perubahan sikap dan perilaku hingga menimbulkan hilangnya kemampuan untuk mengurus diri sendiri. Di negara berpenghasilan tinggi penyakit ini diakui berada pada peringkat ke 7 sebagai penyakit fatal yang berujung pada kematian. Akan tetapi hingga saat ini belum ditemukan obat yang dapat menyembuhkan penyakit Alzheimer. Oleh sebab itu pentingnya deteksi dini agar dapat memulai untuk merencanakan perawatan dan kebutuhan medis yang memadai. Penelitian ini bertujuan untuk melakukan deteksi penyakit Alzheimer dengan menerapkan metode klasifikasi NaÃ¯ve Bayes dan seleksi atribut menggunakan Correlation Based Feature Selection pada dataset OASIS Longitudinal. Tahapan analisa data menggunakan metode CRISP-DM. Hasil penelitian ini, menunjukan bahwa pada pengujian algoritma NaÃ¯ve Bayes nilai akurasi yang didapatkan sebesar 93,83%, dan kurva ROC yang terbentuk memiliki nilai AUC sebesar 0,937% sedangkan pada pengujian algoritma NaÃ¯ve Bayes dan Correlation Based Feature Selection menghasilkan nilai akurasi sebesar 94,64% dan kurva ROC yang terbentuk memiliki nilai AUC sebesar 0,945%. Sehingga dapat disimpulkan bahwa penerapan algoritma NaÃ¯ve Bayes dan metode Correlation Based Feature Selection dapat meningkatkan nilai akurasi.

Penentuan Faktor Kelayakan Penerimaan Karyawan Menggunakan Algoritma Decision Tree pada Perusahaan PT. Personel Alih Daya Deni Anugrah Sahputra; M. Rangga Ramadhan Saelan; Lilyani Asri Utami; Windu Gata
Jurnal Sains dan Informatika Vol. 6 No. 2 (2020): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v6i2.225

Kebutuhan pekerjaan yang semakin meningkat, membuat pelamar pekerjaan semakin meningkat[2]. PT Personel Alih Daya (PERSADA) adalah salah satu outsourcing di Indonesia yang sudah memiliki 19 kantor cabang. membutuhkan banyak kandidat untuk pemenuhan tenaga kerja di Mitra. oleh karena itu diadakan walkin interview dari hari Senin sampai dengan Jumat. Untuk mendapatan tenaga kerja yang memenuhi standar kualifikasi mitra. Pelamar kerja yang datang (walk in interview) ke PT Personel Alih Daya (PERSADA) rata-rata mencapai 50 orang setiap harinya. Untuk memaksimalkan penentuan kelayakan penerimaan tenaga kerja, serta terpenuhnya kebutuhaan tenaga kerja semakin cepat, maka diperlukannya sebuah prediksi cepat untuk mengetahui kelayakan penerimaan karyawan menggunakan algoritma decission tree pada perusahaan PT. Personel Alih Daya. Untuk menentukan kelayakan tenaga kerja baru bisa di klasifikasikan dengan cepat menggunakan prediksi berdasarkan kriteria paling berpengaruh dalam menentukan kelayakan penerimaan karyawan. dilakukan dengan menggunakan metode C4.5 artibut yang digunakan dalam penelitian tersebut berdasarkan umur, domisili, jenis kelamin, Posisi kerja, jurusan, referensi, level pendidikan dan hasil test. Dengan algoritma tersebut menghasilkan angka akurasi 73,27 serta dengan angka curva AUC 0,789.

Komparasi Algoritma Klasifikasi untuk Prediksi Minat Sekolah Tinggi Pelajar pada Students Alcohol Consumption M. Rangga Ramadhan Saelan; Deni Anugrah Sahputra; Widiastuti Widiastuti; Windu Gata
Jurnal Sains dan Informatika Vol. 6 No. 2 (2020): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v6i2.236

MULTIMEDIA LEARNING FOR WUDHU AND SHOLAT PROCEDURES ANDROID BASED AT TK PERTIWI 01 SERANG Widiastuti Widiastuti; Siti Masturoh; Ahmad Hafidzul Kahfi; M Rangga Ramadhan Saelan; Ridan Nurfalah; Muhammad Hilman Fakhriza
Jurnal Techno Nusa Mandiri Vol 17 No 1 (2020): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Wudhu is one way to purify oneself from uncleanness and suffering. Performing ablution perfectly in accordance with Islamic Shari'a is the key to receiving prayer. The introduction of religious activities such as ablution and prayer from an early age is considered necessary. Learning ablution and prayer is usually done by parents repeatedly and by example. In one study, 8 out of 10 children aged 5-6 years did not recognize ablution when they were praying. The method of developing multimedia systems by Luther-Sutopo is one of the system development methods used by multimedia application developers. Therefore it will be built an Android operating learning media that uses Adobe Flash technology to display an animated image, motion, and audio in a 2-dimensional form. This learning media will display 2-dimensional objects of ablution movements, namely intentions, washing both feet and prayer after ablution, and prayer movements from beginning to end and added a few daily prayers. The results of this study are in the form of learning applications for ablution and five-time prayer based on Android. In this application using elements of text, images, animations, and sounds to attract and make it easier for children to remember lessons on how to perform ablution and prayer and various kinds of daily prayers.

COMPARISON OF ACCURACY MEASUREMENTS IN MOTION SENSORS AND HEART RATE MEASUREMENTS USING ANALYTICAL HIERARCHY PROCESS METHODS Tomi Lifti Novier; Nurmalasari Nurmalasari; Widi Astuti; Siti Masturoh; M. Rangga Ramadhan Saelan
Jurnal Techno Nusa Mandiri Vol 18 No 2 (2021): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v18i2.2547

The use of motion sensors in measuring heart rate using smartwatch applications is currently a trend. Everyone is very helpful for measuring their own heart rate. This research is about the comparison of accuracy in motion sensors and measuring heart rate using the Analytical Hierarchy Process (AHP) method. Every technology and application in motion sensor measurement in heart rate measurement has almost the same features and uses as Xiaomi, Samsung, and Apple Inc. From the calculations carried out by the researcher, it shows that the field/stadium that is the most chosen by the community (respondents) is by Random Sampling, with the acquisition of a value of 0.490 aka 49.00%. The second is Treadmill with a value of 0.294 aka 29.40%. the overall value is 0.216 aka 21.60% The alternative that is most chosen by the community (respondents) is the field/stadium. The Analytical Hierarchy Process method can make it easier for prospective technology users to be able to measure the accuracy of motion sensors and detect heart rates, the AHP method makes product decisions based on criteria and alternatives contained in the hierarchy, the results of the study are Apple Inc. as the respondent's choice for technology that is trusted to measure better accuracy on the motion sensor and measure heart rate.

FINAL GRADE PREDICTION MODEL BASED ON STUDENT'S ALCOHOL CONSUMPTION rangga ramadhan saelan; Siti Masturoh; Taopik Hidayat; Siti Nurlela; Risca Lusiana Pratiwi; Muhammad Iqbal
Jurnal Techno Nusa Mandiri Vol 19 No 1 (2022): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.3056

Untuk mengetahui pengaruh konsumsi alcohol dan dan beberapa faktor lainnya yang diperkirakan memiliki peran terhadap tingkat kinerja belajar remaja yang masih bersekolah, maka saat ini dilakukan penelitian terhadap data publik yang telah didapatkan dengan menggunakan teknik machine learning dengan melatih beberapa model untuk memprediksi nilai akhir sebagai acuan kinerja belajar pelajar. Dengan melatih beberapa model machine learning untuk memprediksi nilai tahun akhir dari bahasa portugal dengan melakukan metode komparatif membandingkan model Support Vector Regressor (SVR) dan Random Forest (RF) sehingga akan didapatkan model terbaik untuk memprediksi. Semua model memiliki hyperparameter yang harus disesuaikan. Untuk menyetel hyperparameter ini menggunakan menggunakan Cross Validation. Model terbaik untuk memprediksi nilai akhir G3 adalah Support Vector Regressor (SVR) dan Random Forest (RF), dan memiliki mean absolute error (MAE) masing-masing sekitar 2,24 dan 2,25. Melalui plot MAE, model SVR dan RF bekerja dengan baik. Tetapi, Dengan menganalisis distribusi kesalahan yang dibuat oleh kedua model, dapat disimpulkan bahwa SVR lebih seimbang, yaitu memiliki rasio yang lebih baik antara nilai yang diremehkan dan ditaksir terlalu tinggi, sementara RF berkinerja lebih baik pada outlier.

OPTIMASI HYPERPARAMETER MULTILAYER PERCEPTRON UNTUK PREDIKSI DAYA BELI MOBIL Muhammad Iqbal; Hendri Mahmud Nawawi; M Rangga Ramadhan Saelan; Muhammad Sony Maulana; Yudhistira; Ali Mustopa
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 6 No. 1 (2023): MISI Januari 2023
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v6i1.739

Dalam memutuskan untuk membeli mobil biasanya beberapa faktor dijadikan pertimbangan untuk menentukan keputusan akhir. Maka dari itu sejumlah faktor pendukung seperti harga, type, merk, dan lain sebagainya penting untuk diperhatikan. Pada penelitian ini optimasi model algoritma multilayer perceptron digunakan untuk memodelkan prediksi daya beli mobil konsumen dari dataset publik yang bersumber dari kaggle untuk menemukan model paling optimal terhadap keputusan membeli mobil. Multilayer perceptron sering diterapkan untuk meneliti data yang kompleks karena mampu mengnalisa data dengan baik. Prediksi niat beli tidak hanya dapat mengurangi biaya dealer mobil, tetapi juga mempengaruhi strategi pemasaran dealer mobil dalam jangka panjang. Pengujian menggunakan model Multilayer Perceptron (MLP) dengan konfigurasi default dan hypertuning parameter dilakukan dengan membandingkan dua parameter optimasi yang berbeda yaitu parameter Adam dan RMSprop. Hasilnya didapatkan evaluasi optimal dari konfigurasi default pada parameter optimasi Adam dengan maksimum learning rate 0.01 dengan akurasi 89.50% dan 87,50% untuk optimasi RMSprop sedangkan pengujian dengan konfigurasi hyperparameter tuning dengan dua parameter optimasi yang sama Adam dan RMSprop dengan nilai maksimum learning rate 0,001 didapatkan akurasi sebesar 92.00% untuk parameter RMSprop dan 91,5% pada parameter Adam.

Classification for Papaya Fruit Maturity Level with Convolutional Neural Network Nurmalasari Nurmalasari; Yusuf Arif Setiawan; Widi Astuti; M Rangga Ramadhan Saelan; Siti Masturoh; Tuti Haryanti
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.541

Papaya California (Carica papaya L) is one of the agricultural commodities in the tropics and has a very big opportunity to develop in Indonesia as an agribusiness venture with quite promising prospects. So the quality of papaya fruit is determined by the level of maturity of the fruit, the hardness of the fruit, and its appearance. Papaya fruit undergoes a marked change in color during the ripening process, which indicates chemical changes in the fruit. The change in papaya color from green to yellow is due to the loss of chlorophyll. During storage, the papaya fruit is initially green, then turns slightly yellow. The longer the storage color, the changes to mature the yellow. The process of classifying papaya fruit's ripeness level is usually done manually by business actors, that is, by simply looking at the color of the papaya with the normal eye. Based on the problems that exist in classifying the ripeness level of papaya fruit, in this research, we create a system that can be used to classify papaya fruit skin color using a digital image processing approach. The method used to classify the maturity level of papaya fruit is the Convolutional Neural Network (CNN) Architecture to classify the texture and color of the fruit. This study uses eight transfer learning architectures with 216 simulations with parameter constraints such as optimizer, learning rate, batch size, number of layers, epoch, and dense and can classify the ripeness level of the papaya fruit with a fairly high accuracy of 97%. Farmers use the results of the research in classifying papaya fruit to be harvested by differentiating the maturity level of the fruit more accurately and maintaining the quality of the papaya fruit.

APPLICATION OF THE K-NEAREST NEIGHBOR (KNN) ALGORITHM IN SENTIMENT ANALYSIS OF THE OVO E-WALLET APPLICATION Siti Masturoh; Risca Lusiana Pratiwi; M Rangga Ramadhan Saelan; Ummu Radiyah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 8 No. 2 (2023): JITK Issue February 2023
Publisher : LPPM Nusa Mandiri

Abstract— The OVO application can be downloaded on the Android platform via Google Play, Google play has a review feature on the application product to be downloaded, so that the review can be viewed or accessed by anyone, With these reviews, potential users of the application will see how important it is to consider using an application, problems regarding reviews or sentiment analysis of applications processed using text mining. The purpose of this study is to provide information to prospective OVO application users before using the application which can be seen from the results of giving reviews based on rating or stars (*) in the OVO application review column on Google Play and the authors categorize them into 3 classes, the first class ( 1 to 5 stars, second class (1 and 5 stars) third class by providing labeling grouping (1&2 stars are negative labels, 3 stars are neutral labels and 4&5 stars are positive labels) testing using the k-nearest neighbor method by finding the value of k from the k value of 1-10 to get the highest accuracy value, in order to obtain the highest accuracy value of 84.86% in the 2nd class test and giving a value of k 1 which means that the 1st and 5th star tests get positive values so that they can give a good impression to prospective application users OVO

Classification for Papaya Fruit Maturity Level With Convolutional Neural Network Nurmalasari Nurmalasari; Yusuf Arif Setiawan; Widi Astuti; M. Rangga Ramadhan Saelan; Siti Masturoh; Tuti Haryanti
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Papaya California (Carica papaya L) is one of the agricultural commodities in the tropics and has a very big opportunity to develop in Indonesia as an agribusiness venture with quite promising prospects. So the quality of papaya fruit is determined by the level of maturity of the fruit, the hardness of the fruit, and its appearance. Papaya fruit undergoes a marked change in color during the ripening process, which indicates chemical changes in the fruit. The change in papaya color from green to yellow is due to the loss of chlorophyll. The papaya fruit is initially green during storage, then turns slightly yellow. The longer the storage color, the changes to mature the yellow. The process of classifying papaya fruit's ripeness level is usually done manually by business actors, that is, by simply looking at the color of the papaya with the normal eye. Based on the problems that exist in classifying the ripeness level of papaya fruit, in this research, we create a system that can be used to classify papaya fruit skin color using a digital image processing approach. The method used to classify the maturity level of papaya fruit is the Convolutional Neural Network (CNN) Architecture to classify the texture and color of the fruit. This study uses eight transfer learning architectures with 216 simulations with parameter constraints such as optimizer, learning rate, batch size, number of layers, epoch, and dense and can classify the ripeness level of the papaya fruit with a fairly high accuracy of 97%. Farmers use the results of the research in classifying papaya fruit to be harvested by differentiating the maturity level of the fruit more accurately and maintaining the quality of the papaya fruit.

Co-Authors Agus Subekti Agustiani, Sarifah Ahmad Hafidzul Kahfi Ali Mustopa, Ali Anasanti, Mila Desi Antonius Yadi Kuntoro Daniati Uki Eka Saputri Deni Anugrah Sahputra DEWI RAHMAWATI Gata, Windu Hasanah, Riyan Latifahul Hendri Mahmud Nawawi Lilyani Asri Utami, Lilyani Asri Muhammad Hilman Fakhriza Muhammad Iqbal Muhammad Sony Maulana Nawawi, Hendri Mahmud Nugraha, Fitra Septia Nurfalah, Ridan Nurmalasari Nurmalasari Nurwijayanti Pratiwi, Risca Lusiana Reynaldy, Muhammad Fahreza Ridan Nurfalah Risca Lusiana Pratiwi Risca Lusiana Pratiwi Setiawan, Kanaya Salsabila Siti Khotimatul Wildah Siti Masturoh Siti Masturoh Siti Masturoh Siti Masturoh siti nurlela Taopik Hidayat Tomi Lifti Novier Tuti Haryanti Ummu Radiyah Widiastuti Widiastuti Widiastuti Widiastuti Widiastuti Widiastuti Windu Gata Yudhistira Yudhistira Yusuf Arif Setiawan

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