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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika STMIK Antar Bangsa J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Sistem Informasi (JSI) Jurnal AbdiMas Nusa Mandiri J-SAKTI (Jurnal Sains Komputer dan Informatika)
Frisma Handayanna
Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Energy Environmental Science Mathematics Physics Social Sciences Other

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The Application Of The Luftman Method Toward The Alignment Of Business Strategies And IT In Kelapa Dua Sub-district West Jakarta Handayani, Rani Irma; Handayanna, Frisma; Sari, Fitri Ratna
Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 3 No 2 (2019): SinkrOn Volume 3 Number 2, April 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (499.615 KB) | DOI: 10.33395/sinkron.v3i2.10045

Abstract

The Kelapa Dua West Jakarta is a government agency that has to serve the community every day. In its operational activities, it requires Information Technology (IT) to complete daily operational tasks. Because of this, the west Jakarta sub-district of Kelapa Dua needs an appropriate IT management so that it can serve the community to the fullest. Good IT management is done by assessing the suitability between IT applications and organizational business processes. For this reason, the Luftman method is used to measure the alignment of Business and IT strategies by using six criteria namely Communications, Competency / Value Measurement, Governance, Partnership, Scope & Architecture, Skill. Overall, all the criteria and maturity of information technology strategies and business strategies in the kelurahan are still at level 2 or at the Commited level so that it can be said that there is no alignment between business strategies and IT strategies
SISTEM INFORMASI AKADEMIK LEMBAGA KURSUS KOMPUTER PT. ARETANET INDONESIA AS, Usman; Junaidi, Agus; Handayanna, Frisma
Jurnal Sistem Informasi Vol 4 No 1 (2015)
Publisher : STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (591.789 KB)

Abstract

Abstract-Nonformal education needs of the community will continue to increase. Many factors lead to an increase in the needs of peoples lives. Society changes very quickly lead to the results obtained in school education or formal education, be no longer appropriate or behind the new demands of the world of work. Institute courses are part of the process of nonformal education to improve the quality and productivity of human resources. With the course institution is expected to help the community and this should be in line with the information obtained by the education community. PT. Aretanet Indonesia is a company engaged in the field of education. PT. Aretanet Indonesia practically the course that provides computer training for all kinds of people and all kinds of computer programming.Academic information system design is intended to facilitate participants, lecturers and the institution computer course foreseeing the information long enough, here I facilitate participants, lecturers and the institute of computer courses with academic information system or online, so as to efficiency and effective.Intisari—Kebutuhan masyarakat akan pendidikan nonformal terus mengalami peningkatan. Banyak faktor yang mendorong terjadinya peningkatan kebutuhan dalam kehidupan masyarakat. Perubahan masyarakat yang sangat cepat menyebabkan hasil pendidikan yang diperoleh di sekolah atau pendidikan formal menjadi tidak sesuai lagi atau tertinggal dari tuntutan baru dalam dunia kerja. Lembaga kursus merupakan bagian dari proses pendidikan nonformal untuk meningkatkan kualitas dan produktifitas sumber daya manusia. Dengan adanya lembaga kursus diharapkan dapat membantu masyarakat dan ini harus sejalan dengan informasi yang diperoleh oleh masyarakat tentang pendidikan tersebut. PT. Aretanet Indonesia adalah sebuah perusahaan yang bergerak dalam bidang pendidikan. PT. Aretanet Indonesia bisa dibilang tempat kursus yang menyediakan training komputer untuk semua jenis kalangan dan semua jenis pemograman komputer. Perancangan sistem informasi akademik ini bertujuan untuk mempermudah peserta, pengajar dan pihak lembaga kursus komputer yang sebelumnya mengetahui informasi yang cukup lama, disini saya mempermudah peserta, pengajar dan pihak lembaga kursus komputer dengan sistem informasi akademik secara atau online, sehingga dapat mengefisiensi dan efektif.Kata kunci : Sistem Informasi, Akademik, Lembaga Kursus
PREDIKSI PENYAKIT DIA BETES MELLITUS DENGAN METODE SUPPORT VECTOR MACHINE BERBASIS PARTICLE SWARM OPTIMIZATION Handayanna, Frisma
Jurnal Teknik Informatika Vol. 2 No. 1 (2016): JTI Periode Februari 2016
Publisher : LPPM STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51998/jti.v2i1.5

Abstract

Abstract—Diabetes at this time has increased the number of its patient. Diabetes is a disease that can cause complications even can causes death. In this research made model algorithm Support Vector Machines and model algorithm Support Vector Machines based on Particle Swarm Optimization to get the rule to predict the disease diabetes and give a more accurate value of the accuracy. Because there are still a lot of research using Support Vector Machines in predicting diabetes but the accuracy of the resulting value is still less accurate.. After the testing with two models that Support Vector Machines algorithms and Support Vector Machines based on Particle Swarm Optimization and so test the results are by using Support Vector Machines are get accuracy values 74.21% and AUC values was 0.758, while testing by Support Vector Machines based particle swarm optimization are get value accuracy 77.36 % and value AUC is 0.765 to level diagnose good classification. The two this method having the different levels of accuracy is as much as 3.15 % and the difference in value AUC of 0,017.Intisari—Penyakit diabetes saat ini semakin lama semakin meningkat jumlah penderitanya. Penyakit diabetes adalah salah satu penyakit yang dapat menyebabkan komplikasi bahkan dapat menyebabkan  kematian. Dalam penelitian ini dibuatkan model algoritma Support  Vector Machines dan model algoritma Suppor Vector Machines berbasis Particle Swarm Optimization untuk mendapatkan rule dalam memprediksi penyakit diabetes dan memberikan nilai akurasi yang lebih akurat. Dikarenakan masih banyak penelitian yang menggunakan metode Support  Vector Machines dalam memprediksi penyakit diabetes tetapi nilai akurasi yang dihasilkan masih kurang akurat.  Setelah dilakukan pengujian dengan dua model yaitu Algoritma Support  Vector Machines dan Support Vector Machines berbasis Particle Swarm Optimization maka hasil yang didapat adalah algoritma sehingga didapat pengujian dengan menggunakan Support Vector Machines dimana didapat nilai accuracy adalah 74.21% dan nilai AUC adalah 0.758, sedangkan pengujian dengan menggunakan Support Vector Machines berbasis Particle Swarm Optimization didapatkan nilai accuracy 77.36% dan nilai AUC adalah 0.765 dengan tingkat diagnosa good classification. Sehingga kedua metode tersebut memiliki perbedaan tingkat akurasi yaitu sebesar 3.15% dan perbedaan nilai AUC sebesar 0,017.Kata Kunci— Diabetes, Particle Swarm Optimization, Support Vector Machine.
PREDIKSI PENYAKIT DIA BETES MELLITUS DENGAN METODE SUPPORT VECTOR MACHINE BERBASIS PARTICLE SWARM OPTIMIZATION Handayanna, Frisma
Jurnal Teknik Informatika Vol 2 No 1 (2016): JTI Periode Februari 2016
Publisher : LPPM STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51998/jti.v2i1.5

Abstract

Abstract—Diabetes at this time has increased the number of its patient. Diabetes is a disease that can cause complications even can causes death. In this research made model algorithm Support Vector Machines and model algorithm Support Vector Machines based on Particle Swarm Optimization to get the rule to predict the disease diabetes and give a more accurate value of the accuracy. Because there are still a lot of research using Support Vector Machines in predicting diabetes but the accuracy of the resulting value is still less accurate.. After the testing with two models that Support Vector Machines algorithms and Support Vector Machines based on Particle Swarm Optimization and so test the results are by using Support Vector Machines are get accuracy values 74.21% and AUC values was 0.758, while testing by Support Vector Machines based particle swarm optimization are get value accuracy 77.36 % and value AUC is 0.765 to level diagnose good classification. The two this method having the different levels of accuracy is as much as 3.15 % and the difference in value AUC of 0,017.Intisari—Penyakit diabetes saat ini semakin lama semakin meningkat jumlah penderitanya. Penyakit diabetes adalah salah satu penyakit yang dapat menyebabkan komplikasi bahkan dapat menyebabkan  kematian. Dalam penelitian ini dibuatkan model algoritma Support  Vector Machines dan model algoritma Suppor Vector Machines berbasis Particle Swarm Optimization untuk mendapatkan rule dalam memprediksi penyakit diabetes dan memberikan nilai akurasi yang lebih akurat. Dikarenakan masih banyak penelitian yang menggunakan metode Support  Vector Machines dalam memprediksi penyakit diabetes tetapi nilai akurasi yang dihasilkan masih kurang akurat.  Setelah dilakukan pengujian dengan dua model yaitu Algoritma Support  Vector Machines dan Support Vector Machines berbasis Particle Swarm Optimization maka hasil yang didapat adalah algoritma sehingga didapat pengujian dengan menggunakan Support Vector Machines dimana didapat nilai accuracy adalah 74.21% dan nilai AUC adalah 0.758, sedangkan pengujian dengan menggunakan Support Vector Machines berbasis Particle Swarm Optimization didapatkan nilai accuracy 77.36% dan nilai AUC adalah 0.765 dengan tingkat diagnosa good classification. Sehingga kedua metode tersebut memiliki perbedaan tingkat akurasi yaitu sebesar 3.15% dan perbedaan nilai AUC sebesar 0,017.Kata Kunci— Diabetes, Particle Swarm Optimization, Support Vector Machine.
Pemanfaatan Google Classroom untuk Proses Pembelajaran Siswa Sekolah pada Masa Pandemi Covid-19 Linda Marlinda; Anton Anton; Frisma Handayanna; Susafa’ati Susafa’ati; Taransa Agasya Tutupoly; Faruq Aziz
Jurnal AbdiMas Nusa Mandiri Vol 3 No 1 (2021): Periode April 2021
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/abdimas.v3i1.2032

Abstract

During the Covid-19 pandemic, the government stopped offline learning and replaced it as distance learning that was done online. This distance learning system is the perfect solution during a pandemic like this time so that permanent teaching and learning activities can be used, well and save students so that they are always at home and minimize the occurrence of Covid-19 transmission. One of these obligations is that the teacher carries out Community Service, this is carried out in relation to the concern of every institution or institution engaged in the field of education, especially Higher Education. Almost all fields are affected by this corona, as well as students and schoolgirls in the Bekasi area, West Java. There are quite a number of students and teachers who have not been able to adapt to the distance learning policy, so this training is held so that teachers and students can make optimal use of the software, especially Google Classroom. At the end of this training, participants can take advantage of and master and know more about online learning with Google Suite, especially the use of Google Classroom. This activity also had a good impact in efforts to increase the penetration of advances in information and communication technology (ICT) for students in Bekasi, this was evident from the positive response they had given during the activity
Sistem Pendukung Keputusan Perekrutan Karyawan Online Untuk Penerimaan Karyawan Dengan Metode MOORA F Fatmawati; Frisma Handayanna; Indah Purnamasari
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 2 (2020): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (794.553 KB) | DOI: 10.30645/j-sakti.v4i2.240

Abstract

One company that always gets a lot of prospective employees is PT. Fullmoon Jaya Abadi is engaged in the wholesale trade of special CCTV material. At PT. Fullmoon Jaya Abadi data obtained from the employee recruitment process still use manual methods, so the HRD (Human Resource Development) division must sort and select applicants one by one. A large number of candidates make the HRD division often have difficulty in choosing prospective employees, and subjectivity occurs when they want to decide so that the company accepts employees who do not fit the criteria set by the company. To overcome the problem of making decisions on employee acceptance at PT. Jaya Fullmoon Abadi is using the Multi-Objective Optimization method based on ratio analysis (MOORA). In research data collected based on the position of prospective employees. The results obtained in this study determine each position to receive as many as three prospective employees, namely Arianto Wibowo, Deny Saputra, and Irdha Febriani Awaliyah Ibrahim in the Sales position. Ilham Akbar N.S, Johan Salim, and Alfi Muhayyar in the Graphic Design position. Agustin Sulistyani, Sri Mulyani, and Serli Damayanti in the Accounting Staff position. Hendri Tanu, Adinda Helena, and Wajum Rodi in the IT Support position. Hartopo and Taufik Hidayat in the position of Sales Project.
Sistem Pendukung Keputusan Perekrutan Karyawan Online Untuk Penerimaan Karyawan Dengan Metode MOORA F Fatmawati; Frisma Handayanna; Indah Purnamasari
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 2 (2020): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v4i2.240

Abstract

One company that always gets a lot of prospective employees is PT. Fullmoon Jaya Abadi is engaged in the wholesale trade of special CCTV material. At PT. Fullmoon Jaya Abadi data obtained from the employee recruitment process still use manual methods, so the HRD (Human Resource Development) division must sort and select applicants one by one. A large number of candidates make the HRD division often have difficulty in choosing prospective employees, and subjectivity occurs when they want to decide so that the company accepts employees who do not fit the criteria set by the company. To overcome the problem of making decisions on employee acceptance at PT. Jaya Fullmoon Abadi is using the Multi-Objective Optimization method based on ratio analysis (MOORA). In research data collected based on the position of prospective employees. The results obtained in this study determine each position to receive as many as three prospective employees, namely Arianto Wibowo, Deny Saputra, and Irdha Febriani Awaliyah Ibrahim in the Sales position. Ilham Akbar N.S, Johan Salim, and Alfi Muhayyar in the Graphic Design position. Agustin Sulistyani, Sri Mulyani, and Serli Damayanti in the Accounting Staff position. Hendri Tanu, Adinda Helena, and Wajum Rodi in the IT Support position. Hartopo and Taufik Hidayat in the position of Sales Project.
Co-Authors Agus Junaidi Anton AS, Usman F Fatmawati Faruq Aziz Fitri Ratna Sari Indah Purnamasari Linda Marlinda Rani Irma Handayani, Rani Irma Susafa’ati Susafa’ati Taransa Agasya Tutupoly