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PEMANFAATAN CITRA SATELIT LANDSAT-7 ETM UNTUK PREDIKSI KERUSAKAN MORFOLOGI SUNGAI BATANGHARI AKIBAT PENAMBANGAN EMAS ILEGAL Marhendi, Teguh; Rasyid, Yuzirwan; Kresnanto, Nindyo Cahyo
Techno Jurnal Ilmu Teknik Vol 16, No 1 (2015): Jurnal Techno Volume 16 No.1 April 2015
Publisher : UMP

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Kondisi Sungai Batanghari saat ini telah banyak mengalami deformasi morfologi sungai akibat banyaknya praktek galian C dan penambangan ilagal di sepanjang tubuh sungai.Â Deformasi morfologi Sungai Batanghari diprediksi dapat menyebabkan kerusakan pada tubuh sungai Batanghari dan mengakibatkan penurunan potensi sumber daya air. Penelitian ini bertujuan untk mengetahui perubahan morfologi Sungai Batanghari akibat kegiatan penambangan emas ilegal disepanjang sungai dan anak-anak sungainya. Pendekatan penelitian ini menggunakan model âstratified purpose samplingâ melalui teknik penginderaan jauh dengan wahana citra satelit Landsat-7 ETM.Berdasarkan hasil analisis, Sungai Batanghari mengalami perubahan fisik baik menyangkut badan sungai, lingkungan sungai maupun kualitas air. Perubahan badan sungai terkait kegiatan penambangan tanpa Ijin (PETI) terjadi di beberapa lokasi baik pada sungai utama maupun pada anak-anak sungai Batanghari seperti di Dharmasyraya, Bungo, Batanghari dan Solok Selatan.Kata Kunci: Penambangan ilegal, Kerusakan Morfologi, Citra landsat-7 ETM, Sungai Batanghari

PENGEMBANGAN ALGORITMA PENCARIAN RUTE DAN PEMBEBANAN LALULINTAS FUZZY Kresnanto, Nindyo Cahyo; Tamin, Ofyar Z.; Bona F, Russ
Jurnal Transportasi Vol 8, No 2 (2008)
Publisher : Jurnal Transportasi

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AbstractÂ The path finding, called the Tree Building, is the main foundation on traffic assignment model. In conventional method, each iteration in route finding process can produce best path or shortest-path. On the contrary, on fuzzy travel cost condition, that process could produce several paths as shortest-path nominations. This paper discussed the route finding using fuzzy travel cost approach. In conventional method, travel cost, as an input in the model, is expressed in deterministic form, while in fuzzy cost, travel cost is represented as range of certain value from under-bound to upper-bound. Path finding process in fuzzy cost was developed based on Dijkstra (1959) algorithm which placed each selected route in certain layers. Layer with selected route is sequenced from âfirstâ best route. This algorithm is named as Dijkstra Multi Layer (DML) Algorithm. At the end, flow assignment has done with calculating fuzzy membership value in each selected route toward to the best route.Â Â Â Keywords: fuzzy route cost, shortest path, layer, Dijkstra Algorithm

KAJIAN MODEL PEMBEBANAN JARINGAN DENGAN FUZZY SYSTEM Kresnanto, Nindyo Cahyo; Tamin, Ofyar Z.
Jurnal Transportasi Vol 6, No 2 (2006)
Publisher : Jurnal Transportasi

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AbstrakFaktor utama dalam model pembebanan jaringan transportasi jalan adalah persepsi pengguna jalan terhadap biaya perjalanan. Persepsi ini sebenarnya kurang realistik jika diasumsikan dengan sebuah nilai yang bersifat tetap atau acak (random), seperti pada User Equilibrium (UE) Trip Assignment Model yang memformulasikan persepsi biaya perjalanan bersifat tetap, dan random number yang digunakan pada Stochastic User Equilibrium (SUE) untuk menggambarkan distribusi persepsi pengguna individu terhadap biaya perjalanan. Pada kondisi riil pembuat perjalanan tidak akan pernah mendapatkan informasi yang tepat tentang biaya perjalanan ini, tetapi seringkali mengatakan bahwa waktu perjalanan dari A ke B âsekitar 10 menitâ, atau mengatakan bahwa âJalan C macetâ, terlihat bahwa âsekitarâ atau âmacetâ merupakan persepsi yang bersifat linguistik dan tidak dapat diukur dengan tepat (tak tertentu).Pendekatan baru dengan Fuzzy System yang memiliki kemampuan untuk memecahkan permasalahan yang bersifat uncertainty mulai diterapkan untuk model pembebanan jaringan. Fuzzy System merupakan sebuah sistem yang digunakan untuk penalaran dalam kondisi yang tak tertentu. Rangkaian penalaran ini dimulai dari sebuah masukan (input) tak tertentu yang dipetakan kedalam sebuah himpunan fuzzy (fuzzy set) dengan menggunakan fungsi keanggotaan fuzzy (fuzzy membership function), kemudian dengan rangkaian logika fuzzy (fuzzy logic) input tersebut dipetakan ke dalam ruang keluaran (output) tertentu. Pada kasus model pembabanan jaringan, input yang bersifat tak tertentu adalah berupa persepsi pengguna terhadap biaya perjalanan dan ruang output-nya adalah rute-rute yang akan dipakai dalam melakukan perjalanan.Pada makalah ini akan dikaji tentang penelitian-penelitian penggunaan Fuzzy System untuk model pembebanan jaringan yang pernah dilakukan, terutama untuk merepresentasikan ruang input-nya yang berupa persepsi pembuat perjalanan terhadap biaya perjalanan. Hasil kajian diharapkan dapat memberikan masukan bagi pengembangan pemodelan transportasi pada umumnya dan khususnya pada pengembangan model pembebanan jaringan transportasi.Kata-kata kunci: model pembebanan jaringan, fuzzy system

MODEL PEMILIHAN RUTE DAN PEMBEBANAN PERJALANAN DENGAN SISTEM FUZZY Cahyo Kresnanto, Nindyo; Z. Tamin, Ofyar; Bona Frazila, Russ
Jurnal Transportasi Vol 9, No 2 (2009)
Publisher : Jurnal Transportasi

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Route selection is a major element of the network assignment model. This process is often also callednetwork tree building. In conventional route selection, each repetition will only produce one best path orshortestpath. Using fuzzy systems approach, travel cost is modeled in fuzzy numbers. Fuzzy numbers arenumbers with certain upper-bound and under-bound limits. In fuzzy conditions, route selection process isexpected to produce several routes that can be nominated as the shortestpath. It means that in each repetition,the finding algorithm will generate more than one best route, starting from the first best route, the second bestroute, to the k best route. The next process is the trip assignment existing (demand side) on the transportationnetwork system (supply side) which will produce a route pattern and traffic flow. Traffic flow allocationbased on the series of best routes and degree of membership of fuzzy numbers.Keywords: fuzzy travel cost, fuzzy shortest path, fuzzy assignment

BIAYA PERJALANAN FUZZY UNTUK PEMBEBANAN LALULINTAS Kresnanto, Nindyo Cahyo; Tamin, Ofyar Z.
Jurnal Transportasi Vol 8, No 1 (2008)
Publisher : Jurnal Transportasi

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AbstractÂ The main factor in trip assignment models is the perception of trip makers about their travel costs. In real condition, trip makers usually do not obtain accurate information to estimate their travel costs. Â Travel costs are usually expressed using a linguistic term, such like âtravel time from A to B is about 10 minutesâ, which can not be measured precisely (values with a specific range). Using fuzzy method, the âaboutâ condition can be formulated in the term of fuzzy set with a value having lower-bound and upper-bound boundaries. The set is called fuzzy-travel-cost. In this paper, the trip assignment using fuzzy-travel-cost is compared with that using the deterministic travel cost. The result shows that fuzy-travel-cost model gives a better result than the deterministic travel cost model.Keywords: trip assignment model, fuzzy-travel-cost, fuzzy-shortest-path

PENGEMBANGAN ALGORITMA PENCARIAN RUTE DAN PEMBEBANAN LALULINTAS FUZZY Kresnanto, Nindyo Cahyo; Tamin, Ofyar Z.; Bona F, Russ
Jurnal Transportasi Vol 8, No 2 (2008)
Publisher : Jurnal Transportasi

AbstractÂ The path finding, called the Tree Building, is the main foundation on traffic assignment model. In conventional method, each iteration in route finding process can produce best path or shortest-path. On the contrary, on fuzzy travel cost condition, that process could produce several paths as shortest-path nominations. This paper discussed the route finding using fuzzy travel cost approach. In conventional method, travel cost, as an input in the model, is expressed in deterministic form, while in fuzzy cost, travel cost is represented as range of certain value from under-bound to upper-bound. Path finding process in fuzzy cost was developed based on Dijkstra (1959) algorithm which placed each selected route in certain layers. Layer with selected route is sequenced from âfirstâ best route. This algorithm is named as Dijkstra Multi Layer (DML) Algorithm. At the end, flow assignment has done with calculating fuzzy membership value in each selected route toward to the best route.Â Â Â Keywords: fuzzy route cost, shortest path, layer, Dijkstra Algorithm

KAJIAN MODEL PEMBEBANAN JARINGAN DENGAN FUZZY SYSTEM Kresnanto, Nindyo Cahyo; Tamin, Ofyar Z.
Jurnal Transportasi Vol 6, No 2 (2006)
Publisher : Jurnal Transportasi

AbstrakFaktor utama dalam model pembebanan jaringan transportasi jalan adalah persepsi pengguna jalan terhadap biaya perjalanan. Persepsi ini sebenarnya kurang realistik jika diasumsikan dengan sebuah nilai yang bersifat tetap atau acak (random), seperti pada User Equilibrium (UE) Trip Assignment Model yang memformulasikan persepsi biaya perjalanan bersifat tetap, dan random number yang digunakan pada Stochastic User Equilibrium (SUE) untuk menggambarkan distribusi persepsi pengguna individu terhadap biaya perjalanan. Pada kondisi riil pembuat perjalanan tidak akan pernah mendapatkan informasi yang tepat tentang biaya perjalanan ini, tetapi seringkali mengatakan bahwa waktu perjalanan dari A ke B âsekitar 10 menitâ, atau mengatakan bahwa âJalan C macetâ, terlihat bahwa âsekitarâ atau âmacetâ merupakan persepsi yang bersifat linguistik dan tidak dapat diukur dengan tepat (tak tertentu).Pendekatan baru dengan Fuzzy System yang memiliki kemampuan untuk memecahkan permasalahan yang bersifat uncertainty mulai diterapkan untuk model pembebanan jaringan. Fuzzy System merupakan sebuah sistem yang digunakan untuk penalaran dalam kondisi yang tak tertentu. Rangkaian penalaran ini dimulai dari sebuah masukan (input) tak tertentu yang dipetakan kedalam sebuah himpunan fuzzy (fuzzy set) dengan menggunakan fungsi keanggotaan fuzzy (fuzzy membership function), kemudian dengan rangkaian logika fuzzy (fuzzy logic) input tersebut dipetakan ke dalam ruang keluaran (output) tertentu. Pada kasus model pembabanan jaringan, input yang bersifat tak tertentu adalah berupa persepsi pengguna terhadap biaya perjalanan dan ruang output-nya adalah rute-rute yang akan dipakai dalam melakukan perjalanan.Pada makalah ini akan dikaji tentang penelitian-penelitian penggunaan Fuzzy System untuk model pembebanan jaringan yang pernah dilakukan, terutama untuk merepresentasikan ruang input-nya yang berupa persepsi pembuat perjalanan terhadap biaya perjalanan. Hasil kajian diharapkan dapat memberikan masukan bagi pengembangan pemodelan transportasi pada umumnya dan khususnya pada pengembangan model pembebanan jaringan transportasi.Kata-kata kunci: model pembebanan jaringan, fuzzy system

MODEL PEMILIHAN RUTE DAN PEMBEBANAN PERJALANAN DENGAN SISTEM FUZZY Cahyo Kresnanto, Nindyo; Z. Tamin, Ofyar; Bona Frazila, Russ
Jurnal Transportasi Vol 9, No 2 (2009)
Publisher : Jurnal Transportasi

Route selection is a major element of the network assignment model. This process is often also callednetwork tree building. In conventional route selection, each repetition will only produce one best path orshortestpath. Using fuzzy systems approach, travel cost is modeled in fuzzy numbers. Fuzzy numbers arenumbers with certain upper-bound and under-bound limits. In fuzzy conditions, route selection process isexpected to produce several routes that can be nominated as the shortestpath. It means that in each repetition,the finding algorithm will generate more than one best route, starting from the first best route, the second bestroute, to the k best route. The next process is the trip assignment existing (demand side) on the transportationnetwork system (supply side) which will produce a route pattern and traffic flow. Traffic flow allocationbased on the series of best routes and degree of membership of fuzzy numbers.Keywords: fuzzy travel cost, fuzzy shortest path, fuzzy assignment

BIAYA PERJALANAN FUZZY UNTUK PEMBEBANAN LALULINTAS Kresnanto, Nindyo Cahyo; Tamin, Ofyar Z.
Jurnal Transportasi Vol 8, No 1 (2008)
Publisher : Jurnal Transportasi

AbstractÂ The main factor in trip assignment models is the perception of trip makers about their travel costs. In real condition, trip makers usually do not obtain accurate information to estimate their travel costs. Â Travel costs are usually expressed using a linguistic term, such like âtravel time from A to B is about 10 minutesâ, which can not be measured precisely (values with a specific range). Using fuzzy method, the âaboutâ condition can be formulated in the term of fuzzy set with a value having lower-bound and upper-bound boundaries. The set is called fuzzy-travel-cost. In this paper, the trip assignment using fuzzy-travel-cost is compared with that using the deterministic travel cost. The result shows that fuzy-travel-cost model gives a better result than the deterministic travel cost model.Keywords: trip assignment model, fuzzy-travel-cost, fuzzy-shortest-path

The effect of knowlege management and talent management on employee performance and the impact on competitive advantage (Survey at private colleges in Kulonprogo district, Yogyakarta) Laras, Titi; Kresnanto, Nindyo Cahyo; Raharti, Rini; Nurwiyanta, Nurwiyanta; Wibowo, Apriyanto Giri
MEC-J (Management and Economics Journal) Vol 3, No 1 (2019)
Publisher : Faculty of Economics, State Islamic University of Maulana Malik Ibrahim Malang

This study aims to examine the effect of talent management and knowledge management on employee performance and the impact on performance of Private Colleges Employees in Kulonprogo, Yogyakarta, either partially or simultaneously. The research method used a survey with a questionnaire measured by a Likert Scale. The population is 104 Lecturers and Education Personnel at three active Private Colleges in Kulonprogo District. The data were analyzed by Lisrel 8.80 software. This study produced seven findings. First, Knowledge Management (X1) significantly affects on Employee Performance. Second, Talent Management (X2) significantly affects on Employee Performance (Y). Third, Knowledge Management (X1) and Talent Management (X2) simultaneously have a significant effect on Employee Performance (Y1). Fourth, Knowledge Management (X1) insignificantly affects on Competitive Advantage (Y2). Fifth, Talent Management (X2) insignificantly affects on Competitive Advantage (Y2). Sixth, Knowledge Management (X1) and Talent Management (X2) simultaneously have a positive and significant effect on Competitive Advantage (Y2). Seventh, Employee Performance (Y1) significantly affects on Competitive Advantage (Y2);

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