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All Journal Semesta Teknika Indonesian Journal on Software Engineering (IJSE) Jurnal Teknik Komputer AMIK BSI Jurnal Pilar Nusa Mandiri Jurnal ULTIMA Computing Jurnal Informatika Kaputama (JIK) Jurnal Mantik INTI Nusa Mandiri JIKA (Jurnal Informatika) Jurnal Infortech Reputasi: Jurnal Rekayasa Perangkat Lunak SPEED - Sentra Penelitian Engineering dan Edukasi J-Intech (Journal of Information and Technology) Jurnal Multidisiplin Madani (MUDIMA) Jurnal Ilmu Komputer Jurnal Manuhara: Pusat Penelitian Ilmu Manajemen Dan Bisnis Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Media Teknologi dan Informatika
Taufik Asra
Universitas BIna Sarana Informatika
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Classification of Student Majors with C4.5 and Naive Bayes Algorithms (Case Study: SMAN 2 Bekasi City) Kuntoro, Antonius Yadi; Hermanto, Hermanto; Asra, Taufik; Syukmana, Ferry; Wahono, Hermanto
Semesta Teknika Vol 23, No 1 (2020): MEI 2020
Publisher : Semesta Teknika

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

School majors conducted in high school are based on interests and these have a goal to provide opportunities for learners to develop the competence of attitudes, skills competence of learners in accordance with interests, talents, and academic ability in a group of scientific subjects.In this research, the researcher uses two algorithm models that is a comparison between the C4.5 algorithm and also the Naive Bayes algorithm. In this study, the data used is the results of school entrance test data and also the data from psychological results for students who have been declared passed the entrance test school SMAN 2 Bekasi City academic year 2018/2019. By comparison of two data mining classification algorithm, can be proved with accuracy result and AUC value from each algorithm that is for Naive Bayes accuracy = 76,43% and AUC value = 0,846, while for algorithm C4.5 accuracy = 70,29% and AUC value = 0.738.
Komparasi Algoritma C4.5, K-NN Dan Naïve Bayes Dalam Penerimaan Karyawan Menggunakan PSO Pada PT. XYZ Taufik Asra
Indonesian Journal on Software Engineering (IJSE) Vol 5, No 2 (2019): IJSE 2019
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijse.v5i2.6959

Abstract

Abstrak: Merekrut karyawan-karyawan dengan kualitas terbaik salah satu tugas HRD untuk membantu memajukan perusahaan. Karyawan dengan kualitas terbaik tersebut diharapkan memberikan kontribusi yang tinggi terhadap perusahaan nantinya. Untuk mendapatkan karyawan terbaik perusahaan menerapkan seleksi penerimaan karyawan yang ketat. Masalah muncul ketika karyawan yang lulus seleksi ternyata mengundurkan diri ketika masa kontraknya belum terpenuhi, bahkan ada yang menghilang pada saat proses training. Hal itu mengakibatkan tingginya turnover pada perusahaan. Sampel data yang digunakan ada 111 data, diambil dari data karyawan yang bertahan lebih dari 12 bulan, daftar karyawan resign dibawah 12 bulan dan calon karyawan yang ditolak. Setelah proses cleansing di dapat 80 data dengan rincian 32 diterima dan 48 ditolak. Dengan algoritma C4.5 dioptimasi menggunakan PSO yang dievaluasi dengan confusion matrix menghasilkan tingkat accuracy 86.25%, precision 80.83% dan recall 68.33% serta grafik AUC 0.530. Dengan algoritma K-NN dioptimasi menggunakan PSO yang dievaluasi dengan confusion matrix menghasikan tingkat accuracy 82,50%, precision 84,33% dan recall 75,00% serta nilai grafik AUC 0,796. Dengan algoritma Naïve Bayes dioptimasi PSO yang dievaluasi dengan confusion matrix menghasikan tingkat accuracy 91,25%, precision 88,50,% dan recall 94,17% serta nilai grafik AUC 0,903. Kata kunci: Penerimaan Karyawan, C4.5, K-NN, Naïve Bayes,PSO
Perancangan Sistem Informasi Peralatan Kesehatan Dengan Metode Waterfall Andi Taufik; Ade Christian; Taufik Asra
JURNAL TEKNIK KOMPUTER Vol 5, No 1 (2019): JTK - Periode Februari 2019
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (412.925 KB) | DOI: 10.31294/jtk.v5i1.4530

Abstract

The development of information technology is very rapid. Computers are tools that are created to facilitate human work for all fields. It cannot be separated from the business field, if it cannot compete by using technological advances, the company will be left behind. At present there are still companies selling medical devices where the sales system still uses manual systems. The manual system has problems such as errors in recording, inaccuracy of reports made and delays in finding the data needed. The design of a computerized information system is the best solution to solve the problems that exist in this company. The system is made using the waterfall method to be easy in the process, and with a computerized system can be achieved an activity that is effective and efficient and accurate. Because it's easier in data processing and faster in presenting the information.
Classification of Student Majors with C4.5 and Naive Bayes Algorithms (Case Study: SMAN 2 Bekasi City) Antonius Yadi Kuntoro; Hermanto Hermanto; Taufik Asra; Ferry Syukmana; Hermanto Wahono
Semesta Teknika Vol 23, No 1 (2020): MEI 2020
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.v23i1.7381

Abstract

School majors conducted in high school are based on interests and these have a goal to provide opportunities for learners to develop the competence of attitudes, skills competence of learners in accordance with interests, talents, and academic ability in a group of scientific subjects.In this research, the researcher uses two algorithm models that is a comparison between the C4.5 algorithm and also the Naive Bayes algorithm. In this study, the data used is the results of school entrance test data and also the data from psychological results for students who have been declared passed the entrance test school SMAN 2 Bekasi City academic year 2018/2019. By comparison of two data mining classification algorithm, can be proved with accuracy result and AUC value from each algorithm that is for Naive Bayes accuracy = 76,43% and AUC value = 0,846, while for algorithm C4.5 accuracy = 70,29% and AUC value = 0.738.
Analisa Pemakaian Alat Kesehatan pada Rumah Sakit Menggunakan Analytical Hierarchy Process (AHP) Hafis Nurdin; Irwan Agus Sobari; Aji Sudibyo; Bambang Wijonarko; Felix Wuryo Handono; Taufik Asra
Jurnal Multidisiplin Madani Vol. 2 No. 1 (2022): January 2022
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (497.461 KB)

Abstract

Medical devices are items that are needed in the medical world. Because too many types of medical devices are used, a comparison was made to choose medical devices that are often used in hospitals, especially by operating room (OT) nurses. So a research was made using the Analytical Hierarchy Process (AHP) method. AHP is an alternative for making decisions that have several goals or criteria. To complete the input, it is assisted by the Expert Choice 11 application. From the results of data processing, it is concluded that for the use of medical devices that are often used in hospitals, there are several influencing factors, namely packaging, quality, usage and indications. While the medical devices that are often used are Pencil Couter, Grounding Plate, Syringe and Gloves.
Application of Hybrid Algorithm in Determining the Shortest Route Between Campus Branches Waeisul Bismi; Windu Gata; Anton .; Taufik Asra
Ultima Computing : Jurnal Sistem Komputer Vol 13 No 1 (2021): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v13i1.1856

Abstract

Traffic congestion in the capital city is a familiar sight for the citizens of the capital, because two-wheeled vehicle users dominate the streets of the capital city as much as 72.8 percent while four-wheeled vehicle users are 26.5 percent. And congestion has a negative impact on the activities of the various citizens of the capital city, both in terms of work and in education. Therefore, an effective solution for road users in the capital city in overcoming congestion is to find the shortest route to get to the destination quickly. The application of the Djikstra algorithm is a solution in this case by determining the shortest route from the origin to the destination in order to get to the destination faster. Therefore, the researcher conducted a case study on the way to the STMIK Nusa Mandiri Kramat 18 campus as the place of origin to the STMIK Nusa Mandiri Jatiwaringin campus as the goal by trying to apply the Djikstra algorithm as a method of finding the shortest route.
KLASIFIKASI KELUHAN PENGGUNA KAI ACCESS UNTUK PEMESANAN TIKET DENGAN ALGORITMA SVM DAN NAÏVE BAYES Hermanto -; Antonius Yadi Kuntoro; Taufik Asra
Jurnal Informatika Vol 6, No 2 (2022): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v6i2.6187

Abstract

Perkembangan dan kemajuan Teknologi Informasi dan Komunikasi (TIK) sangat diperlukan guna untuk memudahkan dan menyelesaikan berbagai masalah yang dihadapi oleh manusia dengan cepat dan singkat. Disamping itu, masyarakat zaman sekarang ingin semuanya serba praktis dan tidak menyita banyak waktu. Salah satu contoh permasalahan sehari-hari yang menjadi perhatian masyarakat sekarang adalah transportasi. Kereta api nampaknya menjadi salah satu alat transportasi favorit orang Indonesia terbukti dengan meningkatnya layanan khusus Kereta Api diberbagai perangkat Android, IOS, dan Windows Phone. Penelitian ini fokus terhadap menganalisa kepuasan pengguna aplikasi KAI Access terhadap pemesanan tiket, Penelitian ini bertujuan untuk menganalisis keluhan pengguna aplikasi KAI Access dalam pemesanan tiket kereta api secara online. Terdapat 1321 komentar positif dan negatif pada pengguna aplikasi kai access untuk keluhan pemesanan tiket. Dengan menggunakan Algoritma SVM dan Naïve Bayes dilakukan perbandingan pengujian atas komentar positif dan negatif tersebut. Dari proses pengujian tersebut didapatkan hasil akurasi dari algoritma SVM nilai akurasi = 73.36% dan nilai AUC = 0.794. sedangkan untuk algoritma Naïve Bayes nilai akurasi dan nilai AUC dari algoritma yaitu untuk SVM nilai akurasi = 67.10% dan nilai AUC = 0.573. Dapat disimpulkan bahwa algoritma yang lebih unggul adalah memiliki nilai akurasi tertinggi adalah Algoritma SVM dibanding dengan algoritma Naïve Bayes.
Pengkajian Penerimaan Dan Penggunaan Aplikasi Transportasi Online (Ride Sharing) Di Jakarta Dan Sekitarnya Taufik Asra - BSI Jakarta; Andi Taufik; Muhammad Rofi’i - STMIK Nusa Mandiri Jakarta
SPEED - Sentra Penelitian Engineering dan Edukasi Vol 10, No 3 (2018): Speed 2018
Publisher : APMMI - Asosiasi Profesi Multimedia Indonwsia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (633.344 KB) | DOI: 10.55181/speed.v10i3.351

Abstract

Abstrak – Pengkajian atas penerimaan dan penggunaan terhadap teknologi telah diungkapkan melalui metode Unified Theory of Acceptance and Use of Technology (UTAUT). Metode lain juga berkembang adalah Technology Acceptance Model (TAM). Keunggulan model UTAUT misalnya adanya faktor pengaruh sosial yang tidak terdapat di TAM. Dari beberapa metode yang ada, ada faktor yang tidak terdapat didalamnya yaitu faktor keamanan. Faktor keamanan akan ditambahkan dalam melengkapi kajian model penerimaan dan penggunaan aplikasi transportasi online di Jakarta dan sekitarnya. Uji statistik dengan metode Structural Equation Modeling (SEM) yang akan dianalisis dengan menggunakan software AMOS. Hasil dari analisis SEM diperoleh bahwa model tidak fit. Hipotesis yang diterima adalah bahwa faktor social mempengaruhi penggunaan actual, dan attitude toward mobile service juga memberikan nilai signifikan atas penerimaan teknologi ini.Kata Kunci: UTAUT, TAM, Ride Sharing, AMOS, SEM Abstract – Assessment of acceptance and use of technology has been disclosed through the Unified Theory of Acceptance and Use of Technology (UTAUT) method. Another method also developed is the Technology Acceptance Model (TAM). The advantages of UTAUT model for example the exixtence of social influence factor that is not found in TAM. From some existing methods, there is a factor thas is not contained in it, ie the security factor. The safety factor will be added in order to completing the study of the model of acceptance and use of online transport applications in Jakarta and surrounding areas. Statistical test with Structural Equation Modeling (SEM) method to be analyzed by using AMOS software. Results from the SEM analysis show that the model is not fit. The accepted hypothesis is that social factors affect the actual use, and the attitude toward mobile service also provide significant value on the acceptance of this technology.Key Word: UTAUT, TAM, Ride Sharing, AMOS, SEM
Klasifikasi Penyakit Daun Kentang Menggunakan Model Logistic Regression Wahyutama Fitri Hidayat; Taufik Asra; Ahmad Setiadi
Indonesian Journal on Software Engineering (IJSE) Vol 8, No 2 (2022): IJSE 2022
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijse.v8i2.14624

Abstract

Tanaman kentang merupakan salah satu jenis umbi-umbian yang ditanam di Indonesia. Budidaya kentang dapat dikatakan tidak selalu sesuai yang diharapkan, serangan hama dan penyakit menjadi salah satu faktor penyebabnya. Sebagai upaya indentifikasi penyakit pada tanaman kentang dilakukan penelitian berdasarkan klasifikasi penyakit daun pada tumbuhan kentang. Penelitian ini berisi tentang membuat suatu sistem untuk identifikasi berdasarkan citra daun pada tanaman kentang menggunakan metode klasifikasi Logistic Regression sedangkan untuk ekstraksi fitur digunakan Resnet50. Tahap perancangan sistem diawali dnegan menggumpulkan data berupa data sekunder mengenai penyakit pada daun tanaman kentang, setelah itu dilakukan fitur ekstraksi, data test dan train (pembagian data), serta menghitung nilai akurasi dan prediksi. Model ini dapat mengidentifikasi berdasarkan citra dimana sehingga menghasilkan luaran berupa nilai akurasi dari penerapan model Logistic Regression dan fitur ekstrasi Resnet50. Berdasarkan percobaan yang telah  dilakukan, menggunakan data latih menghasilkan nilai akurasi sebesar 98%, sedangkan menggunakan seluruh data dengan jumlah 405 citra menghasilkan nilai akurasi sebesar 80%.               Kata kunci: Pemrosesan Gambar, Klasifikasi, Resnet50, Logistic Regression
Prediction of PrivyID Application Comments Use as an Electronic Document (e-doc) using the Ensemble Vote method Riza Fahlapi; Hermanto Hermanto; Taufik Asra; Antonius Yadi Kuntoro; Ridatu Oca Nitra; Lasman Effendi
Jurnal Teknik Komputer AMIK BSI Vol 9, No 1 (2023): JTK Periode Januari 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jtk.v9i1.14245

Abstract

Indonesia is developing one of the more efficient and effective Financial Technology (Fintek) support services innovations by using electronic documents. The Electronic Document provider business that is used as a reference and utilized by fintech companies is PrivyID. In this study, how is the commentary aspect of using the PrivyID application for digital signature services to become a legal electronic document. Web-based application platforms and mobile applications in the community are indispensable for the use of Electronic Documents developed by PrivyID as a service provider in business and personal transactions that are needed by the community. More in-depth research regarding the Prediction of PrivyID Application Comments in Its Use as an Electronic Document (e-doc) taken from 818 data of PrivyID application users. The research was conducted by combining 3 (three) algorithms (k-Nearest Neighbor, Na¨ıve Bayes, and C4.5) in the Ensembles Vote method which resulted in the best Prediction Comment value with an accuracy of 86.80. 
Co-Authors -, Nur Muhammad Hariadi Ade Christian Ahmad Setiadi Ahmad Sinnun, Ahmad Andi Taufik Ani Nuriska Safitri Anton . Antonius Yadi Kuntoro Bakhtiar Rifai, Bakhtiar Bambang Wijonarko Dedi Dwi Saputra Eko Yulianto Fahlapi, Riza Febrianti, Serlyn Felix Wuryo Handono Ferry Syukmana Fitri Latifah, Fitri Gata, Windu Hafis Nurdin Hairadifa, Rifka Putri Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Wahono Irwan Agus Sobari Khiyarana Fayziyah Lasman Effendi Mawalda Azharah Maysaroh, Maysaroh Muhammad Rofi’i - STMIK Nusa Mandiri Jakarta Nadia Septia Aisyah Nurajijah, Nurajijah Ridatu Oca Nitra Ridatu Ocanitra Rikardo Nainggolan, Esron RIZA FAHLAPI Riza Fahlapi Saputra, Dedi Dwi Siti Anisah Siti Fauziah Siti Nur Khasanah Sobari, Irwan Agus Sudibyo, Aji Sukmawati, Alfiani Sulistyowati, Daning Nur Waeisul Bismi Wahono, Hermanto Wahyutama Fitri Hidayat